diff --git a/Ch10-deeplearning-lab.Rmd b/Ch10-deeplearning-lab.Rmd index 4354480..a595313 100644 --- a/Ch10-deeplearning-lab.Rmd +++ b/Ch10-deeplearning-lab.Rmd @@ -1,15 +1,3 @@ ---- -jupyter: - jupytext: - cell_metadata_filter: -all - formats: Rmd,ipynb - main_language: python - text_representation: - extension: .Rmd - format_name: rmarkdown - format_version: '1.2' - jupytext_version: 1.14.7 ---- # Chapter 10 @@ -41,7 +29,7 @@ from sklearn.model_selection import \ (train_test_split, GridSearchCV) ``` - + ### Torch-Specific Imports There are a number of imports for `torch`. (These are not @@ -56,7 +44,7 @@ from torch.optim import RMSprop from torch.utils.data import TensorDataset ``` - + There are several other helper packages for `torch`. For instance, the `torchmetrics` package has utilities to compute various metrics to evaluate performance when fitting @@ -70,7 +58,7 @@ from torchmetrics import (MeanAbsoluteError, from torchinfo import summary ``` - + The package `pytorch_lightning` is a somewhat higher-level interface to `torch` that simplifies the specification and fitting of @@ -82,7 +70,7 @@ from pytorch_lightning import Trainer from pytorch_lightning.loggers import CSVLogger ``` - + In order to reproduce results we use `seed_everything()`. We will also instruct `torch` to use deterministic algorithms where possible. @@ -92,7 +80,7 @@ seed_everything(0, workers=True) torch.use_deterministic_algorithms(True, warn_only=True) ``` - + We will use several datasets shipped with `torchvision` for our examples: a pretrained network for image classification, as well as some transforms used for preprocessing. @@ -125,7 +113,7 @@ from ISLP.torch import (SimpleDataModule, rec_num_workers) ``` - + In addition we have included some helper functions to load the `IMDb` database, as well as a lookup that maps integers @@ -159,7 +147,7 @@ from glob import glob import json ``` - + ## Single Layer Network on Hitters Data We start by fitting the models in Section 10.6 on the `Hitters` data. @@ -213,7 +201,7 @@ hit_lm = LinearRegression().fit(X_train, Y_train) Yhat_test = hit_lm.predict(X_test) np.abs(Yhat_test - Y_test).mean() ``` - + Next we fit the lasso using `sklearn`. We are using mean absolute error to select and evaluate a model, rather than mean squared error. The specialized solver we used in Section 6.5.2 uses only mean squared error. So here, with a bit more work, we create a cross-validation grid and perform the cross-validation directly. @@ -236,7 +224,7 @@ $\lambda$ with an all-zero solution. This value equals the largest absolute inn X_s = scaler.fit_transform(X_train) n = X_s.shape[0] lam_max = np.fabs(X_s.T.dot(Y_train - Y_train.mean())).max() / n -param_grid = {'alpha': np.exp(np.linspace(0, np.log(0.01), 100)) +param_grid = {'lasso__alpha': np.exp(np.linspace(0, np.log(0.01), 100)) * lam_max} ``` Note that we had to transform the data first, since the scale of the variables impacts the choice of $\lambda$. @@ -246,13 +234,13 @@ We now perform cross-validation using this sequence of $\lambda$ values. cv = KFold(10, shuffle=True, random_state=1) -grid = GridSearchCV(lasso, +grid = GridSearchCV(standard_lasso, param_grid, cv=cv, scoring='neg_mean_absolute_error') grid.fit(X_train, Y_train); ``` - + We extract the lasso model with best cross-validated mean absolute error, and evaluate its performance on `X_test` and `Y_test`, which were not used in cross-validation. @@ -291,7 +279,7 @@ class HittersModel(nn.Module): return torch.flatten(self.sequential(x)) ``` - + The `class` statement identifies the code chunk as a declaration for a class `HittersModel` that inherits from the base class `nn.Module`. This base @@ -325,7 +313,7 @@ to disable dropout for when we want to evaluate the model on test data. hit_model = HittersModel(X.shape[1]) ``` - + The object `self.sequential` is a composition of four maps. The first maps the 19 features of `Hitters` to 50 dimensions, introducing $50\times 19+50$ parameters for the weights and *intercept* of the map (often called the *bias*). This layer @@ -335,7 +323,7 @@ trainable parameters is therefore $50\times 19+50+50+1=1051$. - + The package `torchinfo` provides a `summary()` function that neatly summarizes this information. We specify the size of the input and see the size of each tensor as it passes through layers of the network. @@ -376,7 +364,7 @@ Y_test_t = torch.tensor(Y_test.astype(np.float32)) hit_test = TensorDataset(X_test_t, Y_test_t) ``` - + Finally, this dataset is passed to a `DataLoader()` which ultimately passes data into our network. While this may seem like a lot of overhead, this structure is helpful for more @@ -397,7 +385,7 @@ workers might be reasonable (here the max was 16). ```{python} max_num_workers = rec_num_workers() ``` - + The general training setup in `pytorch_lightning` involves training, validation and test data. These are each represented by different data loaders. During each epoch, @@ -422,7 +410,7 @@ hit_dm = SimpleDataModule(hit_train, validation=hit_test) ``` - + Next we must provide a `pytorch_lightning` module that controls the steps performed during the training process. We provide methods for our `SimpleModule()` that simply record the value @@ -436,7 +424,7 @@ hit_module = SimpleModule.regression(hit_model, metrics={'mae':MeanAbsoluteError()}) ``` - + By using the `SimpleModule.regression()` method, we indicate that we will use squared-error loss as in (10.23). We have also asked for mean absolute error to be tracked as well @@ -450,7 +438,7 @@ we will not cover those here in detail. ```{python} hit_logger = CSVLogger('logs', name='hitters') ``` - + Finally we are ready to train our model and log the results. We use the `Trainer()` object from `pytorch_lightning` to do this work. The argument `datamodule=hit_dm` tells the trainer @@ -487,8 +475,8 @@ data using the `test()` method of our trainer. hit_trainer.test(hit_module, datamodule=hit_dm) ``` - - + + The results of the fit have been logged into a CSV file. We can find the results specific to this run in the `experiment.metrics_file_path` attribute of our logger. Note that each time the model is fit, the logger will output @@ -544,7 +532,7 @@ ax.set_ylim([0, 400]) ax.set_xticks(np.linspace(0, 50, 11).astype(int)); ``` - + We can predict directly from the final model, and evaluate its performance on the test data. Before fitting, we call the `eval()` method @@ -562,7 +550,7 @@ preds = hit_module(X_test_t) torch.abs(Y_test_t - preds).mean() ``` - + ### Cleanup In setting up our data module, we had initiated @@ -583,7 +571,7 @@ del(Hitters, hit_trainer, hit_module) ``` - + ## Multilayer Network on the MNIST Digit Data The `torchvision` package comes with a number of example datasets, @@ -602,7 +590,7 @@ data will be downloaded the first time this function is executed, and stored in mnist_train ``` - + There are 60,000 images in the training data and 10,000 in the test data. The images are $28\times 28$, and stored as a matrix of pixels. We need to transform each one into a vector. @@ -628,7 +616,7 @@ mnist_dm = SimpleDataModule(mnist_train, batch_size=256) ``` - + Let’s take a look at the data that will get fed into our network. We loop through the first few chunks of the test dataset, breaking after 2 batches: @@ -640,8 +628,8 @@ for idx, (X_ ,Y_) in enumerate(mnist_dm.train_dataloader()): break ``` - - + + We see that the $X$ for each batch consists of 256 images of size `1x28x28`. Here the `1` indicates a single channel (greyscale). For RGB images such as `CIFAR100` below, we will see that the `1` in the size will be replaced by `3` for the three RGB channels. @@ -668,7 +656,7 @@ class MNISTModel(nn.Module): def forward(self, x): return self._forward(x) ``` - + We see that in the first layer, each `1x28x28` image is flattened, then mapped to 256 dimensions where we apply a ReLU activation with 40% dropout. A second layer maps the first layer’s output down to @@ -680,14 +668,14 @@ the 128 dimensions are mapped down to 10, the number of classes in the mnist_model = MNISTModel() ``` - + We can check that the model produces output of expected size based on our existing batch `X_` above. ```{python} mnist_model(X_).size() ``` - + Let’s take a look at the summary of the model. Instead of an `input_size` we can pass a tensor of correct shape. In this case, we pass through the final batched `X_` from above. @@ -712,7 +700,7 @@ mnist_module = SimpleModule.classification(mnist_model, mnist_logger = CSVLogger('logs', name='MNIST') ``` - + Now we are ready to go. The final step is to supply training data, and fit the model. ```{python} @@ -735,7 +723,7 @@ alternative to actually supplying validation data, like we did for the `Hitters` SGD uses batches of 256 observations in computing the gradient, and doing the arithmetic, we see that an epoch corresponds to 188 gradient steps. - + `SimpleModule.classification()` includes an accuracy metric by default. Other @@ -762,7 +750,7 @@ Once again we evaluate the accuracy using the `test()` method of our trainer. Th mnist_trainer.test(mnist_module, datamodule=mnist_dm) ``` - + Table 10.1 also reports the error rates resulting from LDA (Chapter 4) and multiclass logistic regression. For LDA we refer the reader to Section 4.7.3. Although we could use the `sklearn` function `LogisticRegression()` to fit @@ -839,7 +827,7 @@ cifar_train = TensorDataset(cifar_train_X, cifar_test = TensorDataset(cifar_test_X, torch.tensor(cifar_test.targets)) ``` - + The `CIFAR100` dataset consists of 50,000 training images, each represented by a three-dimensional tensor: each three-color image is represented as a set of three channels, each of which consists of $32\times 32$ eight-bit pixels. We standardize as we did for the @@ -865,10 +853,10 @@ for idx, (X_ ,Y_) in enumerate(cifar_dm.train_dataloader()): break ``` - - + + Before we start, we look at some of the training images; similar code produced -Figure 10.5 on page 447. The example below also illustrates +Figure 10.5 on page 406. The example below also illustrates that `TensorDataset` objects can be indexed with integers --- we are choosing random images from the training data by indexing `cifar_train`. In order to display correctly, we must reorder the dimensions by a call to `np.transpose()`. @@ -917,7 +905,7 @@ class BuildingBlock(nn.Module): return self.pool(self.activation(self.conv(x))) ``` - + Notice that we used the `padding = "same"` argument to `nn.Conv2d()`, which ensures that the output channels have the same dimension as the input channels. There are 32 channels in the first @@ -954,7 +942,7 @@ class CIFARModel(nn.Module): return self.output(val) ``` - + We build the model and look at the summary. (We had created examples of `X_` earlier.) ```{python} @@ -1012,7 +1000,7 @@ cifar_trainer.fit(cifar_module, datamodule=cifar_dm) ``` - + This model can take 10 minutes or more to run and achieves about 42% accuracy on the test data. Although this is not terrible for 100-class data (a random classifier gets 1% accuracy), searching the web we see results around @@ -1041,7 +1029,7 @@ cifar_trainer.test(cifar_module, datamodule=cifar_dm) ``` - + ### Hardware Acceleration As deep learning has become ubiquitous in machine learning, hardware @@ -1102,8 +1090,8 @@ imgs = torch.stack([torch.div(crop(resize(read_image(f))), 255) imgs = normalize(imgs) imgs.size() ``` - - + + We now set up the trained network with the weights we read in code block~6. The model has 50 layers, with a fair bit of complexity. ```{python} @@ -1130,7 +1118,7 @@ We now feed our six images through the fitted network. img_preds = resnet_model(imgs) ``` - + Let’s look at the predicted probabilities for each of the top 3 choices. First we compute the probabilities by applying the softmax to the logits in `img_preds`. Note that we have had to call the `detach()` method on the tensor `img_preds` in order to convert @@ -1141,7 +1129,7 @@ img_probs = np.exp(np.asarray(img_preds.detach())) img_probs /= img_probs.sum(1)[:,None] ``` - + In order to see the class labels, we must download the index file associated with `imagenet`. {This is avalable from the book website and [s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json](https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json).} ```{python} @@ -1153,7 +1141,7 @@ class_labels = class_labels.set_index('idx') class_labels = class_labels.sort_index() ``` - + We’ll now construct a data frame for each image file with the labels with the three highest probabilities as estimated by the model above. @@ -1167,8 +1155,8 @@ for i, imgfile in enumerate(imgfiles): print(img_df.reset_index().drop(columns=['idx'])) ``` - - + + We see that the model is quite confident about `Flamingo.jpg`, but a little less so for the other images. @@ -1184,7 +1172,7 @@ del(cifar_test, cifar_optimizer, cifar_trainer) ``` - + ## IMDB Document Classification We now implement models for sentiment classification (Section 10.4) on the `IMDB` @@ -1282,7 +1270,7 @@ summary(imdb_model, 'num_params']) ``` - + We’ll again use a smaller learning rate for these data, hence we pass an `optimizer` to the @@ -1301,7 +1289,7 @@ imdb_module = SimpleModule.binary_classification( optimizer=imdb_optimizer) ``` - + Having loaded the datasets into a data module and created a `SimpleModule`, the remaining steps are familiar. @@ -1315,14 +1303,14 @@ imdb_trainer = Trainer(deterministic=True, imdb_trainer.fit(imdb_module, datamodule=imdb_dm) ``` - + Evaluating the test error yields roughly 86% accuracy. ```{python} test_results = imdb_trainer.test(imdb_module, datamodule=imdb_dm) test_results ``` - + ### Comparison to Lasso We now fit a lasso logistic regression model @@ -1375,7 +1363,7 @@ for l in lam_val: intercepts.append(logit.intercept_) ``` - + The coefficient and intercepts have an extraneous dimension which can be removed by the `np.squeeze()` function. @@ -1388,7 +1376,7 @@ We’ll now make a plot to compare our neural network results with the lasso. ```{python} -# %%capture +%%capture fig, axes = subplots(1, 2, figsize=(16, 8), sharey=True) for ((X_, Y_), data_, @@ -1444,7 +1432,7 @@ del(imdb_model, imdb_train, imdb_test) ``` - + ## Recurrent Neural Networks In this lab we fit the models illustrated in @@ -1474,7 +1462,7 @@ imdb_seq_dm = SimpleDataModule(imdb_seq_train, ) ``` - + The first layer of the RNN is an embedding layer of size 32, which will be learned during training. This layer one-hot encodes each document as a matrix of dimension $500 \times 10,003$, and then maps these @@ -1486,7 +1474,7 @@ matrix of size $10,003\times 32$; each of the 500 integers in the document are then mapped to the appropriate 32 real numbers by indexing the appropriate rows of this matrix. - + The second layer is an LSTM with 32 units, and the output layer is a single logit for the binary classification task. In the last line of the `forward()` method below, @@ -1517,7 +1505,7 @@ summary(lstm_model, 'num_params']) ``` - + The 10,003 is suppressed in the summary, but we see it in the parameter count, since $10,003\times 32=320,096$. @@ -1542,7 +1530,7 @@ track the test performance as the network is fit, and see that it attains 85% ac ```{python} lstm_trainer.test(lstm_module, datamodule=imdb_seq_dm) ``` - + We once again show the learning progress, followed by cleanup. ```{python} @@ -1568,7 +1556,7 @@ del(lstm_model, imdb_seq_test) ``` - + ### Time Series Prediction We now show how to fit the models in Section 10.5.2 @@ -1585,7 +1573,7 @@ X = pd.DataFrame(StandardScaler( index=NYSE.index) ``` - + Next we set up the lagged versions of the data, dropping any rows with missing values using the `dropna()` method. @@ -1599,7 +1587,7 @@ X.insert(len(X.columns), 'train', NYSE['train']) X = X.dropna() ``` - + Finally, we extract the response, training indicator, and drop the current day’s `DJ_return` and `log_volatility` to predict only from previous day’s data. @@ -1609,8 +1597,8 @@ X = X.drop(columns=['train'] + cols) X.columns ``` - - + + We first fit a simple linear model and compute the $R^2$ on the test data using the `score()` method. @@ -1619,7 +1607,7 @@ M = LinearRegression() M.fit(X[train], Y[train]) M.score(X[~train], Y[~train]) ``` - + We refit this model, including the factor variable `day_of_week`. For a categorical series in `pandas`, we can form the indicators using the `get_dummies()` method. @@ -1692,7 +1680,7 @@ class NYSEModel(nn.Module): return torch.flatten(val) nyse_model = NYSEModel() ``` - + We fit the model in a similar fashion to previous networks. We supply the `fit` function with test data as validation data, so that when we monitor its progress and plot the history function we can see the @@ -1711,7 +1699,7 @@ for mask in [train, ~train]: nyse_train, nyse_test = datasets ``` - + Following our usual pattern, we inspect the summary. ```{python} @@ -1742,7 +1730,7 @@ for idx, (x, y) in enumerate(nyse_dm.train_dataloader()): break ``` - + We follow our previous example for setting up a trainer for a regression problem, requesting the $R^2$ metric to be be computed at each epoch. @@ -1755,7 +1743,7 @@ nyse_module = SimpleModule.regression(nyse_model, metrics={'r2':R2Score()}) ``` - + Fitting the model should by now be familiar. The results on the test data are very similar to the linear AR model. @@ -1769,7 +1757,7 @@ nyse_trainer.test(nyse_module, datamodule=nyse_dm) ``` - + We could also fit a model without the `nn.RNN()` layer by just using a `nn.Flatten()` layer instead. This would be a nonlinear AR model. If in addition we excluded the hidden layer, this would be equivalent to our earlier linear AR model. @@ -1789,7 +1777,7 @@ for mask in [train, ~train]: datasets.append(TensorDataset(X_day_t, Y_t)) day_train, day_test = datasets ``` - + Creating a data module follows a familiar pattern. ```{python} @@ -1800,7 +1788,7 @@ day_dm = SimpleDataModule(day_train, batch_size=64) ``` - + We build a `NonLinearARModel()` that takes as input the 20 features and a hidden layer with 32 units. The remaining steps are familiar. ```{python} @@ -1826,7 +1814,7 @@ nl_module = SimpleModule.regression(nl_model, metrics={'r2':R2Score()}) ``` - + We continue with the usual training steps, fit the model, and evaluate the test error. We see the test $R^2$ is a slight improvement over the linear AR model that also includes `day_of_week`. diff --git a/Ch10-deeplearning-lab.ipynb b/Ch10-deeplearning-lab.ipynb index 218f9a8..d7aabea 100644 --- a/Ch10-deeplearning-lab.ipynb +++ b/Ch10-deeplearning-lab.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "23016bca", + "id": "54eb061f", "metadata": {}, "source": [ "\n", @@ -23,10 +23,9 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "cf431f3f", + "execution_count": null, + "id": "01aad02e", "metadata": { - "execution": {}, "lines_to_next_cell": 2 }, "outputs": [], @@ -49,7 +48,7 @@ }, { "cell_type": "markdown", - "id": "667eff82", + "id": "e6e091b1", "metadata": {}, "source": [ "### Torch-Specific Imports\n", @@ -61,11 +60,9 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "1db00e03", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "267afcbc", + "metadata": {}, "outputs": [], "source": [ "import torch\n", @@ -76,7 +73,7 @@ }, { "cell_type": "markdown", - "id": "b3407495", + "id": "4bdedfbb", "metadata": {}, "source": [ "There are several other helper packages for `torch`. For instance,\n", @@ -89,11 +86,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "3da0a445", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "5548bab1", + "metadata": {}, "outputs": [], "source": [ "from torchmetrics import (MeanAbsoluteError,\n", @@ -103,7 +98,7 @@ }, { "cell_type": "markdown", - "id": "e5c55b19", + "id": "3f1accea", "metadata": {}, "source": [ "The package `pytorch_lightning` is a somewhat higher-level\n", @@ -115,11 +110,9 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "bbbf32fe", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "372edabb", + "metadata": {}, "outputs": [], "source": [ "from pytorch_lightning import Trainer\n", @@ -128,7 +121,7 @@ }, { "cell_type": "markdown", - "id": "cf5ec401", + "id": "0b71a03f", "metadata": {}, "source": [ "In order to reproduce results we use `seed_everything()`. We will also instruct `torch` to use deterministic algorithms\n", @@ -137,20 +130,10 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "3810caf4", - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Global seed set to 0\n" - ] - } - ], + "execution_count": null, + "id": "8b905254", + "metadata": {}, + "outputs": [], "source": [ "from pytorch_lightning import seed_everything\n", "seed_everything(0, workers=True)\n", @@ -159,7 +142,7 @@ }, { "cell_type": "markdown", - "id": "c3dea945", + "id": "d79b33ad", "metadata": {}, "source": [ "We will use several datasets shipped with `torchvision` for our\n", @@ -169,10 +152,9 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "454dc419", + "execution_count": null, + "id": "25b146a6", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -189,7 +171,7 @@ }, { "cell_type": "markdown", - "id": "f7f9578a", + "id": "c8fd4a91", "metadata": {}, "source": [ "We have provided a few utilities in `ISLP` specifically for this lab.\n", @@ -206,11 +188,9 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "cd43a4c6", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "5e669f64", + "metadata": {}, "outputs": [], "source": [ "from ISLP.torch import (SimpleDataModule,\n", @@ -221,7 +201,7 @@ }, { "cell_type": "markdown", - "id": "c5231b9d", + "id": "ae8babe9", "metadata": {}, "source": [ "In addition we have included some helper\n", @@ -237,11 +217,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "eaf84e9c", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "84439147", + "metadata": {}, "outputs": [], "source": [ "from ISLP.torch.imdb import (load_lookup,\n", @@ -252,7 +230,7 @@ }, { "cell_type": "markdown", - "id": "c42bc542", + "id": "ae3546d2", "metadata": {}, "source": [ "Finally, we introduce some utility imports not directly related to\n", @@ -268,10 +246,9 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "d007a49b", + "execution_count": null, + "id": "53732c4a", "metadata": { - "execution": {}, "lines_to_next_cell": 2 }, "outputs": [], @@ -282,7 +259,7 @@ }, { "cell_type": "markdown", - "id": "d0fe1cff", + "id": "b2476aa9", "metadata": {}, "source": [ "## Single Layer Network on Hitters Data\n", @@ -291,10 +268,9 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "9da64364", + "execution_count": null, + "id": "34e04cda", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -305,7 +281,7 @@ }, { "cell_type": "markdown", - "id": "0e80a8c0", + "id": "7efd3fd8", "metadata": {}, "source": [ " We will fit two linear models (least squares and lasso) and compare their performance\n", @@ -320,10 +296,9 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "a2cfe999", + "execution_count": null, + "id": "7453a041", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -335,7 +310,7 @@ }, { "cell_type": "markdown", - "id": "5f0851bc", + "id": "6d7d8a37", "metadata": {}, "source": [ "The `to_numpy()` method above converts `pandas`\n", @@ -348,7 +323,7 @@ }, { "cell_type": "markdown", - "id": "afe4eb28", + "id": "4625dfc4", "metadata": {}, "source": [ "We now split the data into test and training, fixing the random\n", @@ -357,11 +332,9 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "5c600069", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "8debfcad", + "metadata": {}, "outputs": [], "source": [ "(X_train, \n", @@ -375,7 +348,7 @@ }, { "cell_type": "markdown", - "id": "e27a6313", + "id": "bdbfa45a", "metadata": {}, "source": [ "### Linear Models\n", @@ -384,23 +357,10 @@ }, { "cell_type": "code", - "execution_count": 13, - "id": "6ea4f551", - "metadata": { - "execution": {} - }, - "outputs": [ - { - "data": { - "text/plain": [ - "259.71528833146243" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "a52a6652", + "metadata": {}, + "outputs": [], "source": [ "hit_lm = LinearRegression().fit(X_train, Y_train)\n", "Yhat_test = hit_lm.predict(X_test)\n", @@ -409,7 +369,7 @@ }, { "cell_type": "markdown", - "id": "e5fe8c6b", + "id": "f263d822", "metadata": {}, "source": [ "Next we fit the lasso using `sklearn`. We are using\n", @@ -422,11 +382,9 @@ }, { "cell_type": "code", - "execution_count": 14, - "id": "f1b8b3f5", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "8e4afcae", + "metadata": {}, "outputs": [], "source": [ "scaler = StandardScaler(with_mean=True, with_std=True)\n", @@ -437,7 +395,7 @@ }, { "cell_type": "markdown", - "id": "39a58ed0", + "id": "629b1cc3", "metadata": {}, "source": [ "We need to create a grid of values for $\\lambda$. As is common practice, \n", @@ -447,10 +405,9 @@ }, { "cell_type": "code", - "execution_count": 15, - "id": "50ce4171", + "execution_count": null, + "id": "4073cd77", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -458,13 +415,13 @@ "X_s = scaler.fit_transform(X_train)\n", "n = X_s.shape[0]\n", "lam_max = np.fabs(X_s.T.dot(Y_train - Y_train.mean())).max() / n\n", - "param_grid = {'alpha': np.exp(np.linspace(0, np.log(0.01), 100))\n", + "param_grid = {'lasso__alpha': np.exp(np.linspace(0, np.log(0.01), 100))\n", " * lam_max}" ] }, { "cell_type": "markdown", - "id": "2cdc3810", + "id": "45126760", "metadata": {}, "source": [ "Note that we had to transform the data first, since the scale of the variables impacts the choice of $\\lambda$.\n", @@ -473,17 +430,15 @@ }, { "cell_type": "code", - "execution_count": 16, - "id": "94c4ab75", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "9cc45ddc", + "metadata": {}, "outputs": [], "source": [ "cv = KFold(10,\n", " shuffle=True,\n", " random_state=1)\n", - "grid = GridSearchCV(lasso,\n", + "grid = GridSearchCV(standard_lasso,\n", " param_grid,\n", " cv=cv,\n", " scoring='neg_mean_absolute_error')\n", @@ -492,7 +447,7 @@ }, { "cell_type": "markdown", - "id": "e5262d51", + "id": "4411b3c2", "metadata": {}, "source": [ "We extract the lasso model with best cross-validated mean absolute error, and evaluate its\n", @@ -502,24 +457,12 @@ }, { "cell_type": "code", - "execution_count": 17, - "id": "86e45999", + "execution_count": null, + "id": "c82dee46", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "text/plain": [ - "257.2382010799497" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "trained_lasso = grid.best_estimator_\n", "Yhat_test = trained_lasso.predict(X_test)\n", @@ -528,7 +471,7 @@ }, { "cell_type": "markdown", - "id": "1f905d89", + "id": "83daf823", "metadata": {}, "source": [ "This is similar to the results we got for the linear model fit by least squares. However, these results can vary a lot for different train/test splits; we encourage the reader to try a different seed in code block 12 and rerun the subsequent code up to this point.\n", @@ -545,11 +488,9 @@ }, { "cell_type": "code", - "execution_count": 18, - "id": "00ac7606", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "1e5e4655", + "metadata": {}, "outputs": [], "source": [ "class HittersModel(nn.Module):\n", @@ -570,7 +511,7 @@ }, { "cell_type": "markdown", - "id": "a25aab3b", + "id": "99b04b42", "metadata": {}, "source": [ "The `class` statement identifies the code chunk as a\n", @@ -605,11 +546,9 @@ }, { "cell_type": "code", - "execution_count": 19, - "id": "bb7ff7e9", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "ff3d0646", + "metadata": {}, "outputs": [], "source": [ "hit_model = HittersModel(X.shape[1])\n" @@ -617,7 +556,7 @@ }, { "cell_type": "markdown", - "id": "ac0c6bf7", + "id": "1229d2e4", "metadata": {}, "source": [ "The object `self.sequential` is a composition of four maps. The\n", @@ -632,7 +571,7 @@ }, { "cell_type": "markdown", - "id": "9e8e69ac", + "id": "dd83b59c", "metadata": {}, "source": [ "The package `torchinfo` provides a `summary()` function that neatly summarizes\n", @@ -642,44 +581,12 @@ }, { "cell_type": "code", - "execution_count": 20, - "id": "b60d34e1", + "execution_count": null, + "id": "5318bef9", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "text/plain": [ - "===================================================================================================================\n", - "Layer (type:depth-idx) Input Shape Output Shape Param #\n", - "===================================================================================================================\n", - "HittersModel [175, 19] [175] --\n", - "├─Flatten: 1-1 [175, 19] [175, 19] --\n", - "├─Sequential: 1-2 [175, 19] [175, 1] --\n", - "│ └─Linear: 2-1 [175, 19] [175, 50] 1,000\n", - "│ └─ReLU: 2-2 [175, 50] [175, 50] --\n", - "│ └─Dropout: 2-3 [175, 50] [175, 50] --\n", - "│ └─Linear: 2-4 [175, 50] [175, 1] 51\n", - "===================================================================================================================\n", - "Total params: 1,051\n", - "Trainable params: 1,051\n", - "Non-trainable params: 0\n", - "Total mult-adds (M): 0.18\n", - "===================================================================================================================\n", - "Input size (MB): 0.01\n", - "Forward/backward pass size (MB): 0.07\n", - "Params size (MB): 0.00\n", - "Estimated Total Size (MB): 0.09\n", - "===================================================================================================================" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "summary(hit_model, \n", " input_size=X_train.shape,\n", @@ -690,7 +597,7 @@ }, { "cell_type": "markdown", - "id": "aa499e3f", + "id": "415949bf", "metadata": {}, "source": [ "We have truncated the end of the output slightly, here and in subsequent uses.\n", @@ -711,10 +618,9 @@ }, { "cell_type": "code", - "execution_count": 21, - "id": "42f63682", + "execution_count": null, + "id": "7dfc40df", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -726,7 +632,7 @@ }, { "cell_type": "markdown", - "id": "6e184dcd", + "id": "e9415167", "metadata": {}, "source": [ "We do the same for the test data." @@ -734,11 +640,9 @@ }, { "cell_type": "code", - "execution_count": 22, - "id": "57fbf564", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "d9f17fe9", + "metadata": {}, "outputs": [], "source": [ "X_test_t = torch.tensor(X_test.astype(np.float32))\n", @@ -748,7 +652,7 @@ }, { "cell_type": "markdown", - "id": "09cabaa8", + "id": "4fa47715", "metadata": {}, "source": [ "Finally, this dataset is passed to a `DataLoader()` which ultimately\n", @@ -771,11 +675,9 @@ }, { "cell_type": "code", - "execution_count": 23, - "id": "570bdd73", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "8860952f", + "metadata": {}, "outputs": [], "source": [ "max_num_workers = rec_num_workers()" @@ -783,7 +685,7 @@ }, { "cell_type": "markdown", - "id": "a7bc2151", + "id": "fbfc6738", "metadata": {}, "source": [ "The general training setup in `pytorch_lightning` involves\n", @@ -805,11 +707,9 @@ }, { "cell_type": "code", - "execution_count": 24, - "id": "c08a4d6d", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "054a982c", + "metadata": {}, "outputs": [], "source": [ "hit_dm = SimpleDataModule(hit_train,\n", @@ -821,7 +721,7 @@ }, { "cell_type": "markdown", - "id": "db5447fa", + "id": "c4052d23", "metadata": {}, "source": [ "Next we must provide a `pytorch_lightning` module that controls\n", @@ -835,11 +735,9 @@ }, { "cell_type": "code", - "execution_count": 25, - "id": "aaa1e593", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "876d67bf", + "metadata": {}, "outputs": [], "source": [ "hit_module = SimpleModule.regression(hit_model,\n", @@ -848,7 +746,7 @@ }, { "cell_type": "markdown", - "id": "8500a2ba", + "id": "fdb19046", "metadata": {}, "source": [ " By using the `SimpleModule.regression()` method, we indicate that we will use squared-error loss as in\n", @@ -864,11 +762,9 @@ }, { "cell_type": "code", - "execution_count": 26, - "id": "1a4e9b3c", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "2b8e22cb", + "metadata": {}, "outputs": [], "source": [ "hit_logger = CSVLogger('logs', name='hitters')" @@ -876,7 +772,7 @@ }, { "cell_type": "markdown", - "id": "77e3c7a5", + "id": "6d89d602", "metadata": {}, "source": [ "Finally we are ready to train our model and log the results. We\n", @@ -897,769 +793,12 @@ }, { "cell_type": "code", - "execution_count": 27, - "id": "2f839fde", + "execution_count": null, + "id": "3989bae8", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: True\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "\n", - " | Name | Type | Params\n", - "---------------------------------------\n", - "0 | model | HittersModel | 1.1 K \n", - "1 | loss | MSELoss | 0 \n", - "---------------------------------------\n", - "1.1 K Trainable params\n", - "0 Non-trainable params\n", - "1.1 K Total params\n", - "0.004 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "57f1417773bf4bab9e04d2641c7de91f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - 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"output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=50` reached.\n" - ] - } - ], + "outputs": [], "source": [ "hit_trainer = Trainer(deterministic=True,\n", " max_epochs=50,\n", @@ -1671,7 +810,7 @@ }, { "cell_type": "markdown", - "id": "4018f616", + "id": "9e24e95b", "metadata": {}, "source": [ "At each step of SGD, the algorithm randomly selects 32 training observations for\n", @@ -1686,57 +825,19 @@ }, { "cell_type": "code", - "execution_count": 28, - "id": "672b4410", + "execution_count": null, + "id": "c550b424", "metadata": { - "execution": {}, "lines_to_next_cell": 2 }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ce00caf66b9d4b4a9bef45097da41604", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " Test metric DataLoader 0\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " test_loss 125020.2421875\n", - " test_mae 250.8108367919922\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" - ] - }, - { - "data": { - "text/plain": [ - "[{'test_loss': 125020.2421875, 'test_mae': 250.8108367919922}]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "hit_trainer.test(hit_module, datamodule=hit_dm)\n" ] }, { "cell_type": "markdown", - "id": "0f4e3b11", + "id": "13d4f77c", "metadata": {}, "source": [ "The results of the fit have been logged into a CSV file. We can find the\n", @@ -1751,11 +852,9 @@ }, { "cell_type": "code", - "execution_count": 29, - "id": "8cf6ef60", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "10123be1", + "metadata": {}, "outputs": [], "source": [ "hit_results = pd.read_csv(hit_logger.experiment.metrics_file_path)" @@ -1763,7 +862,7 @@ }, { "cell_type": "markdown", - "id": "537efe03", + "id": "55ab41db", "metadata": {}, "source": [ "Since we will produce similar plots in later examples, we write a\n", @@ -1772,10 +871,9 @@ }, { "cell_type": "code", - "execution_count": 30, - "id": "67ce1e26", + "execution_count": null, + "id": "ec25fa4e", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -1808,7 +906,7 @@ }, { "cell_type": "markdown", - "id": "53309bb0", + "id": "89edbe2d", "metadata": {}, "source": [ "We now set up our axes, and use our function to produce the MAE plot." @@ -1816,24 +914,12 @@ }, { "cell_type": "code", - "execution_count": 31, - "id": "deb684d2", + "execution_count": null, + "id": "73594a6c", "metadata": { - "execution": {}, "lines_to_next_cell": 2 }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = subplots(1, 1, figsize=(6, 6))\n", "ax = summary_plot(hit_results,\n", @@ -1847,7 +933,7 @@ }, { "cell_type": "markdown", - "id": "eab05619", + "id": "33376f19", "metadata": {}, "source": [ "We can predict directly from the final model, and\n", @@ -1864,24 +950,12 @@ }, { "cell_type": "code", - "execution_count": 32, - "id": "454033dd", + "execution_count": null, + "id": "6b2a88d4", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor(250.8108, grad_fn=)" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "hit_model.eval() \n", "preds = hit_module(X_test_t)\n", @@ -1890,7 +964,7 @@ }, { "cell_type": "markdown", - "id": "b3625ff5", + "id": "30318631", "metadata": {}, "source": [ " " @@ -1898,7 +972,7 @@ }, { "cell_type": "markdown", - "id": "f46e3883", + "id": "3ffae543", "metadata": {}, "source": [ "### Cleanup\n", @@ -1911,10 +985,9 @@ }, { "cell_type": "code", - "execution_count": 33, - "id": "71b3d0d0", + "execution_count": null, + "id": "5f2b7011", "metadata": { - "execution": {}, "lines_to_next_cell": 2 }, "outputs": [], @@ -1932,7 +1005,7 @@ }, { "cell_type": "markdown", - "id": "28d1c832", + "id": "f1a9dc62", "metadata": {}, "source": [ "## Multilayer Network on the MNIST Digit Data\n", @@ -1945,28 +1018,10 @@ }, { "cell_type": "code", - "execution_count": 34, - "id": "def8605c", - "metadata": { - "execution": {} - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Dataset MNIST\n", - " Number of datapoints: 60000\n", - " Root location: data\n", - " Split: Train\n", - " StandardTransform\n", - "Transform: ToTensor()" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "48bc629a", + "metadata": {}, + "outputs": [], "source": [ "(mnist_train, \n", " mnist_test) = [MNIST(root='data',\n", @@ -1979,7 +1034,7 @@ }, { "cell_type": "markdown", - "id": "95ffc346", + "id": "bd95960f", "metadata": {}, "source": [ "There are 60,000 images in the training data and 10,000 in the test\n", @@ -2002,11 +1057,9 @@ }, { "cell_type": "code", - "execution_count": 35, - "id": "8b9e2b8c", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "ed5990a6", + "metadata": {}, "outputs": [], "source": [ "mnist_dm = SimpleDataModule(mnist_train,\n", @@ -2018,7 +1071,7 @@ }, { "cell_type": "markdown", - "id": "91256a1b", + "id": "f734a6fb", "metadata": {}, "source": [ "Let’s take a look at the data that will get fed into our network. We loop through the first few\n", @@ -2027,24 +1080,12 @@ }, { "cell_type": "code", - "execution_count": 36, - "id": "a4b95dc6", + "execution_count": null, + "id": "eef728ee", "metadata": { - "execution": {}, "lines_to_next_cell": 2 }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "X: torch.Size([256, 1, 28, 28])\n", - "Y: torch.Size([256])\n", - "X: torch.Size([256, 1, 28, 28])\n", - "Y: torch.Size([256])\n" - ] - } - ], + "outputs": [], "source": [ "for idx, (X_ ,Y_) in enumerate(mnist_dm.train_dataloader()):\n", " print('X: ', X_.shape)\n", @@ -2055,7 +1096,7 @@ }, { "cell_type": "markdown", - "id": "12e7eddb", + "id": "b596f7cd", "metadata": {}, "source": [ "We see that the $X$ for each batch consists of 256 images of size `1x28x28`.\n", @@ -2067,11 +1108,9 @@ }, { "cell_type": "code", - "execution_count": 37, - "id": "17714c25", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "b5b33e69", + "metadata": {}, "outputs": [], "source": [ "class MNISTModel(nn.Module):\n", @@ -2096,7 +1135,7 @@ }, { "cell_type": "markdown", - "id": "9893ffb2", + "id": "b104c09a", "metadata": {}, "source": [ "We see that in the first layer, each `1x28x28` image is flattened, then mapped to\n", @@ -2109,11 +1148,9 @@ }, { "cell_type": "code", - "execution_count": 38, - "id": "88a4bf46", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "61f919db", + "metadata": {}, "outputs": [], "source": [ "mnist_model = MNISTModel()\n" @@ -2121,7 +1158,7 @@ }, { "cell_type": "markdown", - "id": "049febff", + "id": "fc14d02c", "metadata": {}, "source": [ "We can check that the model produces output of expected size based\n", @@ -2130,30 +1167,17 @@ }, { "cell_type": "code", - "execution_count": 39, - "id": "ea0d9387", - "metadata": { - "execution": {} - }, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([256, 10])" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "c2a025dd", + "metadata": {}, + "outputs": [], "source": [ "mnist_model(X_).size()" ] }, { "cell_type": "markdown", - "id": "638026d1", + "id": "8be19fe2", "metadata": {}, "source": [ "Let’s take a look at the summary of the model. Instead of an `input_size` we can pass\n", @@ -2163,48 +1187,10 @@ }, { "cell_type": "code", - "execution_count": 40, - "id": "17c34a29", - "metadata": { - "execution": {} - }, - "outputs": [ - { - "data": { - "text/plain": [ - "===================================================================================================================\n", - "Layer (type:depth-idx) Input Shape Output Shape Param #\n", - "===================================================================================================================\n", - "MNISTModel [256, 1, 28, 28] [256, 10] --\n", - "├─Sequential: 1-1 [256, 1, 28, 28] [256, 10] --\n", - "│ └─Sequential: 2-1 [256, 1, 28, 28] [256, 256] --\n", - "│ │ └─Flatten: 3-1 [256, 1, 28, 28] [256, 784] --\n", - "│ │ └─Linear: 3-2 [256, 784] [256, 256] 200,960\n", - "│ │ └─ReLU: 3-3 [256, 256] [256, 256] --\n", - "│ │ └─Dropout: 3-4 [256, 256] [256, 256] --\n", - "│ └─Sequential: 2-2 [256, 256] [256, 128] --\n", - "│ │ └─Linear: 3-5 [256, 256] [256, 128] 32,896\n", - "│ │ └─ReLU: 3-6 [256, 128] [256, 128] --\n", - "│ │ └─Dropout: 3-7 [256, 128] [256, 128] --\n", - "│ └─Linear: 2-3 [256, 128] [256, 10] 1,290\n", - "===================================================================================================================\n", - "Total params: 235,146\n", - "Trainable params: 235,146\n", - "Non-trainable params: 0\n", - "Total mult-adds (M): 60.20\n", - "===================================================================================================================\n", - "Input size (MB): 0.80\n", - "Forward/backward pass size (MB): 0.81\n", - "Params size (MB): 0.94\n", - "Estimated Total Size (MB): 2.55\n", - "===================================================================================================================" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "f5cc08d2", + "metadata": {}, + "outputs": [], "source": [ "summary(mnist_model,\n", " input_data=X_,\n", @@ -2215,7 +1201,7 @@ }, { "cell_type": "markdown", - "id": "73e3cd00", + "id": "135f8472", "metadata": {}, "source": [ "Having set up both the model and the data module, fitting this model is\n", @@ -2227,11 +1213,9 @@ }, { "cell_type": "code", - "execution_count": 41, - "id": "a0608bd1", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "5d1c8aaa", + "metadata": {}, "outputs": [], "source": [ "mnist_module = SimpleModule.classification(mnist_model,\n", @@ -2241,7 +1225,7 @@ }, { "cell_type": "markdown", - "id": "6959c893", + "id": "be8c08a1", "metadata": {}, "source": [ "Now we are ready to go. The final step is to supply training data, and fit the model." @@ -2249,489 +1233,12 @@ }, { "cell_type": "code", - "execution_count": 42, - "id": "cf8e3d9d", + "execution_count": null, + "id": "6ae62c97", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: True\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "\n", - " | Name | Type | Params\n", - "-------------------------------------------\n", - "0 | model | MNISTModel | 235 K \n", - "1 | loss | CrossEntropyLoss | 0 \n", - "-------------------------------------------\n", - "235 K Trainable params\n", - "0 Non-trainable params\n", - "235 K Total params\n", - "0.941 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "63df57862e174cfbac1b87dcdce84e5b", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0c8fa4bf11bc406ca04772133304a80b", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "286d40af4ac7465fa859e111186e92a2", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b34683237d33463fb3e956721f9e93a0", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": 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"metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0984ca944941484e93e601290273e964", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0e31a106652a4d97bcaa03c8bb92d0a3", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "5f752e49bf3f4e6498a650df67fac555", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=30` reached.\n" - ] - } - ], + "outputs": [], "source": [ "mnist_trainer = Trainer(deterministic=True,\n", " max_epochs=30,\n", @@ -2743,7 +1250,7 @@ }, { "cell_type": "markdown", - "id": "cc8724fa", + "id": "a8e09221", "metadata": {}, "source": [ "We have suppressed the output here, which is a progress report on the\n", @@ -2761,7 +1268,7 @@ }, { "cell_type": "markdown", - "id": "b9dc38ac", + "id": "bd3d052a", "metadata": {}, "source": [ "`SimpleModule.classification()` includes\n", @@ -2773,24 +1280,12 @@ }, { "cell_type": "code", - "execution_count": 43, - "id": "45e03385", + "execution_count": null, + "id": "a44b6e2a", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "mnist_results = pd.read_csv(mnist_logger.experiment.metrics_file_path)\n", "fig, ax = subplots(1, 1, figsize=(6, 6))\n", @@ -2805,7 +1300,7 @@ }, { "cell_type": "markdown", - "id": "1679f357", + "id": "7cbfe2a6", "metadata": {}, "source": [ "Once again we evaluate the accuracy using the `test()` method of our trainer. This model achieves\n", @@ -2814,49 +1309,10 @@ }, { "cell_type": "code", - "execution_count": 44, - "id": "3a875b78", - "metadata": { - "execution": {} - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "7d992d3d2f4c42a19871f4bff88c3af3", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " Test metric DataLoader 0\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " test_accuracy 0.9656999707221985\n", - " test_loss 0.15191325545310974\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" - ] - }, - { - "data": { - "text/plain": [ - "[{'test_loss': 0.15191325545310974, 'test_accuracy': 0.9656999707221985}]" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "4d5f5f12", + "metadata": {}, + "outputs": [], "source": [ "mnist_trainer.test(mnist_module,\n", " datamodule=mnist_dm)" @@ -2864,7 +1320,7 @@ }, { "cell_type": "markdown", - "id": "12ef4787", + "id": "e4b8bf50", "metadata": {}, "source": [ "Table 10.1 also reports the error rates resulting from LDA (Chapter 4) and multiclass logistic\n", @@ -2877,11 +1333,9 @@ }, { "cell_type": "code", - "execution_count": 45, - "id": "2f035d83", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "e5671ae9", + "metadata": {}, "outputs": [], "source": [ "class MNIST_MLR(nn.Module):\n", @@ -2900,491 +1354,12 @@ }, { "cell_type": "code", - "execution_count": 46, - "id": "2cd67ad8", + "execution_count": null, + "id": "a23e2901", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: True\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1_Ch10/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:67: UserWarning: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `pytorch_lightning` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n", - " warning_cache.warn(\n", - "\n", - " | Name | Type | Params\n", - "-------------------------------------------\n", - "0 | model | MNIST_MLR | 7.9 K \n", - "1 | loss | CrossEntropyLoss | 0 \n", - "-------------------------------------------\n", - "7.9 K Trainable params\n", - "0 Non-trainable params\n", - "7.9 K Total params\n", - "0.031 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "bfb904663981447bb712602e62891de1", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e3364eae3d824a26aaf19ba7738f28b9", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - 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"model_id": "22c7db100b94443bb460b48280327587", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=30` reached.\n" - ] - } - ], + "outputs": [], "source": [ "mlr_trainer = Trainer(deterministic=True,\n", " max_epochs=30,\n", @@ -3394,7 +1369,7 @@ }, { "cell_type": "markdown", - "id": "20a316a1", + "id": "884f093f", "metadata": {}, "source": [ "We fit the model just as before and compute the test results." @@ -3402,50 +1377,12 @@ }, { "cell_type": "code", - "execution_count": 47, - "id": "3900d16e", + "execution_count": null, + "id": "ed74a18f", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c57bc8ba68d144729b19ba47f0d2c05d", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " Test metric DataLoader 0\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " test_accuracy 0.9235000014305115\n", - " test_loss 0.3271547853946686\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" - ] - }, - { - "data": { - "text/plain": [ - "[{'test_loss': 0.3271547853946686, 'test_accuracy': 0.9235000014305115}]" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "mlr_trainer.test(mlr_module,\n", " datamodule=mnist_dm)" @@ -3453,7 +1390,7 @@ }, { "cell_type": "markdown", - "id": "ca80aa75", + "id": "377ad7e3", "metadata": {}, "source": [ "The accuracy is above 90% even for this pretty simple model.\n", @@ -3464,10 +1401,9 @@ }, { "cell_type": "code", - "execution_count": 48, - "id": "679f2ea5", + "execution_count": null, + "id": "eb0a9b7c", "metadata": { - "execution": {}, "lines_to_next_cell": 2 }, "outputs": [], @@ -3486,7 +1422,7 @@ }, { "cell_type": "markdown", - "id": "dd8ba586", + "id": "6a4d8f68", "metadata": {}, "source": [ "## Convolutional Neural Networks\n", @@ -3496,21 +1432,10 @@ }, { "cell_type": "code", - "execution_count": 49, - "id": "e4af6604", - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Files already downloaded and verified\n", - "Files already downloaded and verified\n" - ] - } - ], + "execution_count": null, + "id": "b6b59dc9", + "metadata": {}, + "outputs": [], "source": [ "(cifar_train, \n", " cifar_test) = [CIFAR100(root=\"data\",\n", @@ -3521,11 +1446,9 @@ }, { "cell_type": "code", - "execution_count": 50, - "id": "2b613ecc", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "96cc5b81", + "metadata": {}, "outputs": [], "source": [ "transform = ToTensor()\n", @@ -3541,7 +1464,7 @@ }, { "cell_type": "markdown", - "id": "af1d3cdc", + "id": "90fcaef5", "metadata": {}, "source": [ "The `CIFAR100` dataset consists of 50,000 training images, each represented by a three-dimensional tensor:\n", @@ -3554,10 +1477,9 @@ }, { "cell_type": "code", - "execution_count": 51, - "id": "4b325cb4", + "execution_count": null, + "id": "d32468ad", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -3571,7 +1493,7 @@ }, { "cell_type": "markdown", - "id": "f361276e", + "id": "04b4f304", "metadata": {}, "source": [ "We again look at the shape of typical batches in our data loaders." @@ -3579,24 +1501,12 @@ }, { "cell_type": "code", - "execution_count": 52, - "id": "cb3d00cb", + "execution_count": null, + "id": "e6894a25", "metadata": { - "execution": {}, "lines_to_next_cell": 2 }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "X: torch.Size([128, 3, 32, 32])\n", - "Y: torch.Size([128])\n", - "X: torch.Size([128, 3, 32, 32])\n", - "Y: torch.Size([128])\n" - ] - } - ], + "outputs": [], "source": [ "for idx, (X_ ,Y_) in enumerate(cifar_dm.train_dataloader()):\n", " print('X: ', X_.shape)\n", @@ -3607,11 +1517,11 @@ }, { "cell_type": "markdown", - "id": "08771862", + "id": "ca410e56", "metadata": {}, "source": [ "Before we start, we look at some of the training images; similar code produced\n", - "Figure 10.5 on page 447. The example below also illustrates\n", + "Figure 10.5 on page 406. The example below also illustrates\n", "that `TensorDataset` objects can be indexed with integers --- we are choosing\n", "random images from the training data by indexing `cifar_train`. In order to display correctly,\n", "we must reorder the dimensions by a call to `np.transpose()`." @@ -3619,24 +1529,12 @@ }, { "cell_type": "code", - "execution_count": 53, - "id": "60d09656", + "execution_count": null, + "id": "6d02e07d", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, axes = subplots(5, 5, figsize=(10,10))\n", "rng = np.random.default_rng(4)\n", @@ -3654,7 +1552,7 @@ }, { "cell_type": "markdown", - "id": "642140af", + "id": "2fcf64b3", "metadata": {}, "source": [ "Here the `imshow()` method recognizes from the shape of its argument that it is a 3-dimensional array, with the last dimension indexing the three RGB color channels.\n", @@ -3670,11 +1568,9 @@ }, { "cell_type": "code", - "execution_count": 54, - "id": "f823da11", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "1a48403c", + "metadata": {}, "outputs": [], "source": [ "class BuildingBlock(nn.Module):\n", @@ -3697,7 +1593,7 @@ }, { "cell_type": "markdown", - "id": "00927159", + "id": "fa812f56", "metadata": {}, "source": [ "Notice that we used the `padding = \"same\"` argument to\n", @@ -3717,11 +1613,9 @@ }, { "cell_type": "code", - "execution_count": 55, - "id": "1a172f7e", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "d72cc6f0", + "metadata": {}, "outputs": [], "source": [ "class CIFARModel(nn.Module):\n", @@ -3747,7 +1641,7 @@ }, { "cell_type": "markdown", - "id": "8455079f", + "id": "e38608dc", "metadata": {}, "source": [ "We build the model and look at the summary. (We had created examples of `X_` earlier.)" @@ -3755,60 +1649,12 @@ }, { "cell_type": "code", - "execution_count": 56, - "id": "651e62b4", + "execution_count": null, + "id": "7617292c", "metadata": { - "execution": {}, "lines_to_next_cell": 2 }, - "outputs": [ - { - "data": { - "text/plain": [ - "===================================================================================================================\n", - "Layer (type:depth-idx) Input Shape Output Shape Param #\n", - "===================================================================================================================\n", - "CIFARModel [128, 3, 32, 32] [128, 100] --\n", - "├─Sequential: 1-1 [128, 3, 32, 32] [128, 256, 2, 2] --\n", - "│ └─BuildingBlock: 2-1 [128, 3, 32, 32] [128, 32, 16, 16] --\n", - "│ │ └─Conv2d: 3-1 [128, 3, 32, 32] [128, 32, 32, 32] 896\n", - "│ │ └─ReLU: 3-2 [128, 32, 32, 32] [128, 32, 32, 32] --\n", - "│ │ └─MaxPool2d: 3-3 [128, 32, 32, 32] [128, 32, 16, 16] --\n", - "│ └─BuildingBlock: 2-2 [128, 32, 16, 16] [128, 64, 8, 8] --\n", - "│ │ └─Conv2d: 3-4 [128, 32, 16, 16] [128, 64, 16, 16] 18,496\n", - "│ │ └─ReLU: 3-5 [128, 64, 16, 16] [128, 64, 16, 16] --\n", - "│ │ └─MaxPool2d: 3-6 [128, 64, 16, 16] [128, 64, 8, 8] --\n", - "│ └─BuildingBlock: 2-3 [128, 64, 8, 8] [128, 128, 4, 4] --\n", - "│ │ └─Conv2d: 3-7 [128, 64, 8, 8] [128, 128, 8, 8] 73,856\n", - "│ │ └─ReLU: 3-8 [128, 128, 8, 8] [128, 128, 8, 8] --\n", - "│ │ └─MaxPool2d: 3-9 [128, 128, 8, 8] [128, 128, 4, 4] --\n", - "│ └─BuildingBlock: 2-4 [128, 128, 4, 4] [128, 256, 2, 2] --\n", - "│ │ └─Conv2d: 3-10 [128, 128, 4, 4] [128, 256, 4, 4] 295,168\n", - "│ │ └─ReLU: 3-11 [128, 256, 4, 4] [128, 256, 4, 4] --\n", - "│ │ └─MaxPool2d: 3-12 [128, 256, 4, 4] [128, 256, 2, 2] --\n", - "├─Sequential: 1-2 [128, 1024] [128, 100] --\n", - "│ └─Dropout: 2-5 [128, 1024] [128, 1024] --\n", - "│ └─Linear: 2-6 [128, 1024] [128, 512] 524,800\n", - "│ └─ReLU: 2-7 [128, 512] [128, 512] --\n", - "│ └─Linear: 2-8 [128, 512] [128, 100] 51,300\n", - "===================================================================================================================\n", - "Total params: 964,516\n", - "Trainable params: 964,516\n", - "Non-trainable params: 0\n", - "Total mult-adds (G): 2.01\n", - "===================================================================================================================\n", - "Input size (MB): 1.57\n", - "Forward/backward pass size (MB): 63.54\n", - "Params size (MB): 3.86\n", - "Estimated Total Size (MB): 68.97\n", - "===================================================================================================================" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "cifar_model = CIFARModel()\n", "summary(cifar_model,\n", @@ -3820,7 +1666,7 @@ }, { "cell_type": "markdown", - "id": "7dd67ce7", + "id": "9fb3e638", "metadata": {}, "source": [ "The total number of trainable parameters is 964,516.\n", @@ -3853,11 +1699,9 @@ }, { "cell_type": "code", - "execution_count": 57, - "id": "63f2650e", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "f07871fb", + "metadata": {}, "outputs": [], "source": [ "cifar_optimizer = RMSprop(cifar_model.parameters(), lr=0.001)\n", @@ -3869,488 +1713,10 @@ }, { "cell_type": "code", - "execution_count": 58, - "id": "a3e4bc28", - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: True\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "\n", - " | Name | Type | Params\n", - "-------------------------------------------\n", - "0 | model | CIFARModel | 964 K \n", - "1 | loss | CrossEntropyLoss | 0 \n", - "-------------------------------------------\n", - "964 K Trainable params\n", - "0 Non-trainable params\n", - "964 K Total params\n", - "3.858 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "cf75ac2267f9488babf1c88067185953", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "30da5d15249f473bb2e55004a4fbc6fc", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "75f26bea217748b1b0bfb0b97b06be25", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": 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"model_id": "150df772fdeb468c8ae3dd482aba37d4", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0d23b1967cfb439eaf23fd1c7d80aa58", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "fd4a4f6be2084d2dabad62591601d0ef", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "4fa594c69b7e4fb485f6c5ef8b4468f1", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=30` reached.\n" - ] - } - ], + "execution_count": null, + "id": "a1541fe1", + "metadata": {}, + "outputs": [], "source": [ "cifar_trainer = Trainer(deterministic=True,\n", " max_epochs=30,\n", @@ -4362,7 +1728,7 @@ }, { "cell_type": "markdown", - "id": "c3fc9402", + "id": "68da5b69", "metadata": {}, "source": [ "This model can take 10 minutes or more to run and achieves about 42% accuracy on the test\n", @@ -4377,24 +1743,12 @@ }, { "cell_type": "code", - "execution_count": 59, - "id": "6b161d93", + "execution_count": null, + "id": "762509e2", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "log_path = cifar_logger.experiment.metrics_file_path\n", "cifar_results = pd.read_csv(log_path)\n", @@ -4410,7 +1764,7 @@ }, { "cell_type": "markdown", - "id": "71dea0c8", + "id": "18fd032e", "metadata": {}, "source": [ "Finally, we evaluate our model on our test data." @@ -4418,50 +1772,12 @@ }, { "cell_type": "code", - "execution_count": 60, - "id": "ab8a91dc", + "execution_count": null, + "id": "e3fa0dbe", "metadata": { - "execution": {}, "lines_to_next_cell": 2 }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "72fa41a873654201bbc2bbb922da9bc2", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " Test metric DataLoader 0\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " test_accuracy 0.42969998717308044\n", - " test_loss 2.4235761165618896\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" - ] - }, - { - "data": { - "text/plain": [ - "[{'test_loss': 2.4235761165618896, 'test_accuracy': 0.42969998717308044}]" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "cifar_trainer.test(cifar_module,\n", " datamodule=cifar_dm)\n" @@ -4469,7 +1785,7 @@ }, { "cell_type": "markdown", - "id": "11d0e8b3", + "id": "ff9ea96a", "metadata": {}, "source": [ "### Hardware Acceleration\n", @@ -4489,511 +1805,12 @@ }, { "cell_type": "code", - "execution_count": 61, - "id": "6d9962ae", + "execution_count": null, + "id": "b5ab7104", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: True\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "\n", - " | Name | Type | Params\n", - "-------------------------------------------\n", - "0 | model | CIFARModel | 964 K \n", - "1 | loss | CrossEntropyLoss | 0 \n", - "-------------------------------------------\n", - "964 K Trainable params\n", - "0 Non-trainable params\n", - "964 K Total params\n", - "3.858 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "8561f251621b4b4bb80996645ee21113", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6ec66d48c6cb4c3d946961e005a8a479", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1_Ch10/lib/python3.10/site-packages/torchmetrics/functional/classification/accuracy.py:77: UserWarning: MPS: no support for int64 reduction ops, casting it to int32 (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/mps/operations/ReduceOps.mm:144.)\n", - " tp = tp.sum(dim=0 if multidim_average == \"global\" else 1)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "4d4700f8a59d48909fab4b18101147f7", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3818f316eaca4ba495994364785ace29", - 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"output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c531fb2f7d5244ad8d0c0e4a4ee028c7", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b5460433203542fea838b387894252c0", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c7c5e5f98571429b8dd82815442cd626", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=30` reached.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "486106ba7b134b3682fc506c03ebce5e", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "try:\n", " for name, metric in cifar_module.metrics.items():\n", @@ -5011,7 +1828,7 @@ }, { "cell_type": "markdown", - "id": "3faab186", + "id": "e8ae730c", "metadata": {}, "source": [ "This yields approximately two- or three-fold acceleration for each epoch.\n", @@ -5021,7 +1838,7 @@ }, { "cell_type": "markdown", - "id": "31759785", + "id": "e8f4c41a", "metadata": {}, "source": [ "## Using Pretrained CNN Models\n", @@ -5042,24 +1859,12 @@ }, { "cell_type": "code", - "execution_count": 62, - "id": "a71c9acb", + "execution_count": null, + "id": "13e1142b", "metadata": { - "execution": {}, "lines_to_next_cell": 2 }, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([6, 3, 224, 224])" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "resize = Resize((232,232), antialias=True)\n", "crop = CenterCrop(224)\n", @@ -5074,7 +1879,7 @@ }, { "cell_type": "markdown", - "id": "89071e87", + "id": "1205db56", "metadata": {}, "source": [ "We now set up the trained network with the weights we read in code block~6. The model has 50 layers, with a fair bit of complexity." @@ -5082,212 +1887,12 @@ }, { "cell_type": "code", - "execution_count": 63, - "id": "4f890244", + "execution_count": null, + "id": "e1b20c85", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "text/plain": [ - "===================================================================================================================\n", - "Layer (type:depth-idx) Input Shape Output Shape Param #\n", - "===================================================================================================================\n", - "ResNet [6, 3, 224, 224] [6, 1000] --\n", - "├─Conv2d: 1-1 [6, 3, 224, 224] [6, 64, 112, 112] 9,408\n", - "├─BatchNorm2d: 1-2 [6, 64, 112, 112] [6, 64, 112, 112] 128\n", - "├─ReLU: 1-3 [6, 64, 112, 112] [6, 64, 112, 112] --\n", - "├─MaxPool2d: 1-4 [6, 64, 112, 112] [6, 64, 56, 56] --\n", - "├─Sequential: 1-5 [6, 64, 56, 56] [6, 256, 56, 56] --\n", - "│ └─Bottleneck: 2-1 [6, 64, 56, 56] [6, 256, 56, 56] --\n", - "│ │ └─Conv2d: 3-1 [6, 64, 56, 56] [6, 64, 56, 56] 4,096\n", - "│ │ └─BatchNorm2d: 3-2 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", - "│ │ └─ReLU: 3-3 [6, 64, 56, 56] [6, 64, 56, 56] --\n", - "│ │ └─Conv2d: 3-4 [6, 64, 56, 56] [6, 64, 56, 56] 36,864\n", - "│ │ └─BatchNorm2d: 3-5 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", - "│ │ └─ReLU: 3-6 [6, 64, 56, 56] [6, 64, 56, 56] --\n", - "│ │ └─Conv2d: 3-7 [6, 64, 56, 56] [6, 256, 56, 56] 16,384\n", - "│ │ └─BatchNorm2d: 3-8 [6, 256, 56, 56] [6, 256, 56, 56] 512\n", - "│ │ └─Sequential: 3-9 [6, 64, 56, 56] [6, 256, 56, 56] 16,896\n", - "│ │ └─ReLU: 3-10 [6, 256, 56, 56] [6, 256, 56, 56] --\n", - "│ └─Bottleneck: 2-2 [6, 256, 56, 56] [6, 256, 56, 56] --\n", - "│ │ └─Conv2d: 3-11 [6, 256, 56, 56] [6, 64, 56, 56] 16,384\n", - "│ │ └─BatchNorm2d: 3-12 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", - "│ │ └─ReLU: 3-13 [6, 64, 56, 56] [6, 64, 56, 56] --\n", - "│ │ └─Conv2d: 3-14 [6, 64, 56, 56] [6, 64, 56, 56] 36,864\n", - "│ │ └─BatchNorm2d: 3-15 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", - "│ │ └─ReLU: 3-16 [6, 64, 56, 56] [6, 64, 56, 56] --\n", - "│ │ └─Conv2d: 3-17 [6, 64, 56, 56] [6, 256, 56, 56] 16,384\n", - "│ │ └─BatchNorm2d: 3-18 [6, 256, 56, 56] [6, 256, 56, 56] 512\n", - "│ │ └─ReLU: 3-19 [6, 256, 56, 56] [6, 256, 56, 56] --\n", - "│ └─Bottleneck: 2-3 [6, 256, 56, 56] [6, 256, 56, 56] --\n", - "│ │ └─Conv2d: 3-20 [6, 256, 56, 56] [6, 64, 56, 56] 16,384\n", - "│ │ └─BatchNorm2d: 3-21 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", - "│ │ └─ReLU: 3-22 [6, 64, 56, 56] [6, 64, 56, 56] --\n", - "│ │ └─Conv2d: 3-23 [6, 64, 56, 56] [6, 64, 56, 56] 36,864\n", - "│ │ └─BatchNorm2d: 3-24 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", - "│ │ └─ReLU: 3-25 [6, 64, 56, 56] [6, 64, 56, 56] --\n", - "│ │ └─Conv2d: 3-26 [6, 64, 56, 56] [6, 256, 56, 56] 16,384\n", - "│ │ └─BatchNorm2d: 3-27 [6, 256, 56, 56] [6, 256, 56, 56] 512\n", - "│ │ └─ReLU: 3-28 [6, 256, 56, 56] [6, 256, 56, 56] --\n", - "├─Sequential: 1-6 [6, 256, 56, 56] [6, 512, 28, 28] --\n", - "│ └─Bottleneck: 2-4 [6, 256, 56, 56] [6, 512, 28, 28] --\n", - "│ │ └─Conv2d: 3-29 [6, 256, 56, 56] [6, 128, 56, 56] 32,768\n", - "│ │ └─BatchNorm2d: 3-30 [6, 128, 56, 56] [6, 128, 56, 56] 256\n", - "│ │ └─ReLU: 3-31 [6, 128, 56, 56] [6, 128, 56, 56] --\n", - "│ │ └─Conv2d: 3-32 [6, 128, 56, 56] [6, 128, 28, 28] 147,456\n", - "│ │ └─BatchNorm2d: 3-33 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", - "│ │ └─ReLU: 3-34 [6, 128, 28, 28] [6, 128, 28, 28] --\n", - "│ │ └─Conv2d: 3-35 [6, 128, 28, 28] [6, 512, 28, 28] 65,536\n", - "│ │ └─BatchNorm2d: 3-36 [6, 512, 28, 28] [6, 512, 28, 28] 1,024\n", - "│ │ └─Sequential: 3-37 [6, 256, 56, 56] [6, 512, 28, 28] 132,096\n", - "│ │ └─ReLU: 3-38 [6, 512, 28, 28] [6, 512, 28, 28] --\n", - "│ └─Bottleneck: 2-5 [6, 512, 28, 28] [6, 512, 28, 28] --\n", - "│ │ └─Conv2d: 3-39 [6, 512, 28, 28] [6, 128, 28, 28] 65,536\n", - "│ │ └─BatchNorm2d: 3-40 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", - "│ │ └─ReLU: 3-41 [6, 128, 28, 28] [6, 128, 28, 28] --\n", - "│ │ └─Conv2d: 3-42 [6, 128, 28, 28] [6, 128, 28, 28] 147,456\n", - "│ │ └─BatchNorm2d: 3-43 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", - "│ │ └─ReLU: 3-44 [6, 128, 28, 28] [6, 128, 28, 28] --\n", - "│ │ └─Conv2d: 3-45 [6, 128, 28, 28] [6, 512, 28, 28] 65,536\n", - "│ │ └─BatchNorm2d: 3-46 [6, 512, 28, 28] [6, 512, 28, 28] 1,024\n", - "│ │ └─ReLU: 3-47 [6, 512, 28, 28] [6, 512, 28, 28] --\n", - "│ └─Bottleneck: 2-6 [6, 512, 28, 28] [6, 512, 28, 28] --\n", - "│ │ └─Conv2d: 3-48 [6, 512, 28, 28] [6, 128, 28, 28] 65,536\n", - "│ │ └─BatchNorm2d: 3-49 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", - "│ │ └─ReLU: 3-50 [6, 128, 28, 28] [6, 128, 28, 28] --\n", - "│ │ └─Conv2d: 3-51 [6, 128, 28, 28] [6, 128, 28, 28] 147,456\n", - "│ │ └─BatchNorm2d: 3-52 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", - "│ │ └─ReLU: 3-53 [6, 128, 28, 28] [6, 128, 28, 28] --\n", - "│ │ └─Conv2d: 3-54 [6, 128, 28, 28] [6, 512, 28, 28] 65,536\n", - "│ │ └─BatchNorm2d: 3-55 [6, 512, 28, 28] [6, 512, 28, 28] 1,024\n", - "│ │ └─ReLU: 3-56 [6, 512, 28, 28] [6, 512, 28, 28] --\n", - "│ └─Bottleneck: 2-7 [6, 512, 28, 28] [6, 512, 28, 28] --\n", - "│ │ └─Conv2d: 3-57 [6, 512, 28, 28] [6, 128, 28, 28] 65,536\n", - "│ │ └─BatchNorm2d: 3-58 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", - "│ │ └─ReLU: 3-59 [6, 128, 28, 28] [6, 128, 28, 28] --\n", - "│ │ └─Conv2d: 3-60 [6, 128, 28, 28] [6, 128, 28, 28] 147,456\n", - "│ │ └─BatchNorm2d: 3-61 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", - "│ │ └─ReLU: 3-62 [6, 128, 28, 28] [6, 128, 28, 28] --\n", - "│ │ └─Conv2d: 3-63 [6, 128, 28, 28] [6, 512, 28, 28] 65,536\n", - "│ │ └─BatchNorm2d: 3-64 [6, 512, 28, 28] [6, 512, 28, 28] 1,024\n", - "│ │ └─ReLU: 3-65 [6, 512, 28, 28] [6, 512, 28, 28] --\n", - "├─Sequential: 1-7 [6, 512, 28, 28] [6, 1024, 14, 14] --\n", - "│ └─Bottleneck: 2-8 [6, 512, 28, 28] [6, 1024, 14, 14] --\n", - "│ │ └─Conv2d: 3-66 [6, 512, 28, 28] [6, 256, 28, 28] 131,072\n", - "│ │ └─BatchNorm2d: 3-67 [6, 256, 28, 28] [6, 256, 28, 28] 512\n", - "│ │ └─ReLU: 3-68 [6, 256, 28, 28] [6, 256, 28, 28] --\n", - "│ │ └─Conv2d: 3-69 [6, 256, 28, 28] [6, 256, 14, 14] 589,824\n", - "│ │ └─BatchNorm2d: 3-70 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-71 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-72 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-73 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", - "│ │ └─Sequential: 3-74 [6, 512, 28, 28] [6, 1024, 14, 14] 526,336\n", - "│ │ └─ReLU: 3-75 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ └─Bottleneck: 2-9 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ │ └─Conv2d: 3-76 [6, 1024, 14, 14] [6, 256, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-77 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-78 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-79 [6, 256, 14, 14] [6, 256, 14, 14] 589,824\n", - "│ │ └─BatchNorm2d: 3-80 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-81 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-82 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-83 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", - "│ │ └─ReLU: 3-84 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ └─Bottleneck: 2-10 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ │ └─Conv2d: 3-85 [6, 1024, 14, 14] [6, 256, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-86 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-87 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-88 [6, 256, 14, 14] [6, 256, 14, 14] 589,824\n", - "│ │ └─BatchNorm2d: 3-89 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-90 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-91 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-92 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", - "│ │ └─ReLU: 3-93 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ └─Bottleneck: 2-11 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ │ └─Conv2d: 3-94 [6, 1024, 14, 14] [6, 256, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-95 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-96 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-97 [6, 256, 14, 14] [6, 256, 14, 14] 589,824\n", - "│ │ └─BatchNorm2d: 3-98 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-99 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-100 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-101 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", - "│ │ └─ReLU: 3-102 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ └─Bottleneck: 2-12 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ │ └─Conv2d: 3-103 [6, 1024, 14, 14] [6, 256, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-104 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-105 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-106 [6, 256, 14, 14] [6, 256, 14, 14] 589,824\n", - "│ │ └─BatchNorm2d: 3-107 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-108 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-109 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-110 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", - "│ │ └─ReLU: 3-111 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ └─Bottleneck: 2-13 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ │ └─Conv2d: 3-112 [6, 1024, 14, 14] [6, 256, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-113 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-114 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-115 [6, 256, 14, 14] [6, 256, 14, 14] 589,824\n", - "│ │ └─BatchNorm2d: 3-116 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-117 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-118 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-119 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", - "│ │ └─ReLU: 3-120 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "├─Sequential: 1-8 [6, 1024, 14, 14] [6, 2048, 7, 7] --\n", - "│ └─Bottleneck: 2-14 [6, 1024, 14, 14] [6, 2048, 7, 7] --\n", - "│ │ └─Conv2d: 3-121 [6, 1024, 14, 14] [6, 512, 14, 14] 524,288\n", - "│ │ └─BatchNorm2d: 3-122 [6, 512, 14, 14] [6, 512, 14, 14] 1,024\n", - "│ │ └─ReLU: 3-123 [6, 512, 14, 14] [6, 512, 14, 14] --\n", - "│ │ └─Conv2d: 3-124 [6, 512, 14, 14] [6, 512, 7, 7] 2,359,296\n", - "│ │ └─BatchNorm2d: 3-125 [6, 512, 7, 7] [6, 512, 7, 7] 1,024\n", - "│ │ └─ReLU: 3-126 [6, 512, 7, 7] [6, 512, 7, 7] --\n", - "│ │ └─Conv2d: 3-127 [6, 512, 7, 7] [6, 2048, 7, 7] 1,048,576\n", - "│ │ └─BatchNorm2d: 3-128 [6, 2048, 7, 7] [6, 2048, 7, 7] 4,096\n", - "│ │ └─Sequential: 3-129 [6, 1024, 14, 14] [6, 2048, 7, 7] 2,101,248\n", - "│ │ └─ReLU: 3-130 [6, 2048, 7, 7] [6, 2048, 7, 7] --\n", - "│ └─Bottleneck: 2-15 [6, 2048, 7, 7] [6, 2048, 7, 7] --\n", - "│ │ └─Conv2d: 3-131 [6, 2048, 7, 7] [6, 512, 7, 7] 1,048,576\n", - "│ │ └─BatchNorm2d: 3-132 [6, 512, 7, 7] [6, 512, 7, 7] 1,024\n", - "│ │ └─ReLU: 3-133 [6, 512, 7, 7] [6, 512, 7, 7] --\n", - "│ │ └─Conv2d: 3-134 [6, 512, 7, 7] [6, 512, 7, 7] 2,359,296\n", - "│ │ └─BatchNorm2d: 3-135 [6, 512, 7, 7] [6, 512, 7, 7] 1,024\n", - "│ │ └─ReLU: 3-136 [6, 512, 7, 7] [6, 512, 7, 7] --\n", - "│ │ └─Conv2d: 3-137 [6, 512, 7, 7] [6, 2048, 7, 7] 1,048,576\n", - "│ │ └─BatchNorm2d: 3-138 [6, 2048, 7, 7] [6, 2048, 7, 7] 4,096\n", - "│ │ └─ReLU: 3-139 [6, 2048, 7, 7] [6, 2048, 7, 7] --\n", - "│ └─Bottleneck: 2-16 [6, 2048, 7, 7] [6, 2048, 7, 7] --\n", - "│ │ └─Conv2d: 3-140 [6, 2048, 7, 7] [6, 512, 7, 7] 1,048,576\n", - "│ │ └─BatchNorm2d: 3-141 [6, 512, 7, 7] [6, 512, 7, 7] 1,024\n", - "│ │ └─ReLU: 3-142 [6, 512, 7, 7] [6, 512, 7, 7] --\n", - "│ │ └─Conv2d: 3-143 [6, 512, 7, 7] [6, 512, 7, 7] 2,359,296\n", - "│ │ └─BatchNorm2d: 3-144 [6, 512, 7, 7] [6, 512, 7, 7] 1,024\n", - "│ │ └─ReLU: 3-145 [6, 512, 7, 7] [6, 512, 7, 7] --\n", - "│ │ └─Conv2d: 3-146 [6, 512, 7, 7] [6, 2048, 7, 7] 1,048,576\n", - "│ │ └─BatchNorm2d: 3-147 [6, 2048, 7, 7] [6, 2048, 7, 7] 4,096\n", - "│ │ └─ReLU: 3-148 [6, 2048, 7, 7] [6, 2048, 7, 7] --\n", - "├─AdaptiveAvgPool2d: 1-9 [6, 2048, 7, 7] [6, 2048, 1, 1] --\n", - "├─Linear: 1-10 [6, 2048] [6, 1000] 2,049,000\n", - "===================================================================================================================\n", - "Total params: 25,557,032\n", - "Trainable params: 25,557,032\n", - "Non-trainable params: 0\n", - "Total mult-adds (G): 24.54\n", - "===================================================================================================================\n", - "Input size (MB): 3.61\n", - "Forward/backward pass size (MB): 1066.99\n", - "Params size (MB): 102.23\n", - "Estimated Total Size (MB): 1172.83\n", - "===================================================================================================================" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "resnet_model = resnet50(weights=ResNet50_Weights.DEFAULT)\n", "summary(resnet_model,\n", @@ -5299,7 +1904,7 @@ }, { "cell_type": "markdown", - "id": "185bb4b5", + "id": "6110b39b", "metadata": {}, "source": [ "We set the mode to `eval()` to ensure that the model is ready to predict on new data." @@ -5307,206 +1912,19 @@ }, { "cell_type": "code", - "execution_count": 64, - "id": "c4be9922", + "execution_count": null, + "id": "8151bbfb", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "text/plain": [ - "ResNet(\n", - " (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n", - " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n", - " (layer1): Sequential(\n", - " (0): Bottleneck(\n", - " (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", - " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", - " (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " (downsample): Sequential(\n", - " (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", - " (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " )\n", - " )\n", - " (1): Bottleneck(\n", - " (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", - " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", - " (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " )\n", - " (2): Bottleneck(\n", - " (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", - " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", - " (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " )\n", - " )\n", - " (layer2): Sequential(\n", - " (0): Bottleneck(\n", - " (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", - " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", - " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", - " (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - 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"metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "resnet_model.eval()" ] }, { "cell_type": "markdown", - "id": "f0a3c519", + "id": "c9a22edc", "metadata": {}, "source": [ "Inspecting the output above, we see that when setting up the\n", @@ -5518,11 +1936,9 @@ }, { "cell_type": "code", - "execution_count": 65, - "id": "2dc63d85", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "4e7ab2f6", + "metadata": {}, "outputs": [], "source": [ "img_preds = resnet_model(imgs)\n" @@ -5530,7 +1946,7 @@ }, { "cell_type": "markdown", - "id": "15ec1321", + "id": "f846f3b6", "metadata": {}, "source": [ "Let’s look at the predicted probabilities for each of the top 3 choices. First we compute\n", @@ -5541,11 +1957,9 @@ }, { "cell_type": "code", - "execution_count": 66, - "id": "711d5ba7", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "cbc70a8c", + "metadata": {}, "outputs": [], "source": [ "img_probs = np.exp(np.asarray(img_preds.detach()))\n", @@ -5554,7 +1968,7 @@ }, { "cell_type": "markdown", - "id": "3b514c1e", + "id": "93c89b24", "metadata": {}, "source": [ "In order to see the class labels, we must download the index file associated with `imagenet`. {This is avalable from the book website and [s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json](https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json).}" @@ -5562,11 +1976,9 @@ }, { "cell_type": "code", - "execution_count": 67, - "id": "b22f70d8", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "2c2c944a", + "metadata": {}, "outputs": [], "source": [ "labs = json.load(open('imagenet_class_index.json'))\n", @@ -5579,7 +1991,7 @@ }, { "cell_type": "markdown", - "id": "a5812782", + "id": "b002ed28", "metadata": {}, "source": [ "We’ll now construct a data frame for each image file\n", @@ -5589,50 +2001,12 @@ }, { "cell_type": "code", - "execution_count": 68, - "id": "b19c6bd1", + "execution_count": null, + "id": "55109a93", "metadata": { - "execution": {}, "lines_to_next_cell": 2 }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Image: book_images/Cape_Weaver.jpg\n", - " label prob\n", - "0 jacamar 0.298104\n", - "1 macaw 0.068012\n", - "2 lorikeet 0.051065\n", - "Image: book_images/Flamingo.jpg\n", - " label prob\n", - "0 flamingo 0.606624\n", - "1 spoonbill 0.013324\n", - "2 American_egret 0.002130\n", - "Image: book_images/Hawk_Fountain.jpg\n", - " label prob\n", - "0 kite 0.187797\n", - "1 robin 0.084580\n", - "2 great_grey_owl 0.059789\n", - "Image: book_images/Hawk_cropped.jpg\n", - " label prob\n", - "0 kite 0.454471\n", - "1 great_grey_owl 0.015789\n", - "2 jay 0.011916\n", - "Image: book_images/Lhasa_Apso.jpg\n", - " label prob\n", - "0 Lhasa 0.260186\n", - "1 Shih-Tzu 0.097995\n", - "2 Tibetan_terrier 0.032107\n", - "Image: book_images/Sleeping_Cat.jpg\n", - " label prob\n", - "0 Persian_cat 0.163612\n", - "1 tabby 0.074585\n", - "2 tiger_cat 0.042531\n" - ] - } - ], + "outputs": [], "source": [ "for i, imgfile in enumerate(imgfiles):\n", " img_df = class_labels.copy()\n", @@ -5644,7 +2018,7 @@ }, { "cell_type": "markdown", - "id": "cd6bd40b", + "id": "af5e72ea", "metadata": {}, "source": [ "We see that the model\n", @@ -5656,10 +2030,9 @@ }, { "cell_type": "code", - "execution_count": 69, - "id": "ba80b615", + "execution_count": null, + "id": "d522aa4b", "metadata": { - "execution": {}, "lines_to_next_cell": 2 }, "outputs": [], @@ -5675,7 +2048,7 @@ }, { "cell_type": "markdown", - "id": "2e6eafaf", + "id": "9f314168", "metadata": {}, "source": [ "## IMDB Document Classification\n", @@ -5701,25 +2074,12 @@ }, { "cell_type": "code", - "execution_count": 70, - "id": "ba6d2d2c", + "execution_count": null, + "id": "d53facf3", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458,\n", - " 4468], dtype=int32)" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "(imdb_seq_train,\n", " imdb_seq_test) = load_sequential(root='data/IMDB')\n", @@ -5730,7 +2090,7 @@ }, { "cell_type": "markdown", - "id": "ebeeb069", + "id": "cf3dc365", "metadata": {}, "source": [ "The datasets `imdb_seq_train` and `imdb_seq_test` are\n", @@ -5747,23 +2107,10 @@ }, { "cell_type": "code", - "execution_count": 71, - "id": "93bda908", - "metadata": { - "execution": {} - }, - "outputs": [ - { - "data": { - "text/plain": [ - "\" this film was just brilliant casting location scenery story direction everyone's\"" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "0ea1acd0", + "metadata": {}, + "outputs": [], "source": [ "lookup = load_lookup(root='data/IMDB')\n", "' '.join(lookup[i] for i in sample_review)" @@ -5771,7 +2118,7 @@ }, { "cell_type": "markdown", - "id": "6de86e76", + "id": "63d3997a", "metadata": {}, "source": [ "For our first model, we have created a binary feature for each\n", @@ -5783,10 +2130,9 @@ }, { "cell_type": "code", - "execution_count": 72, - "id": "40943b7d", + "execution_count": null, + "id": "19fb4313", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -5803,7 +2149,7 @@ }, { "cell_type": "markdown", - "id": "9328d75c", + "id": "33217683", "metadata": {}, "source": [ "We’ll use a two-layer model for our first model." @@ -5811,10 +2157,9 @@ }, { "cell_type": "code", - "execution_count": 73, - "id": "2117fd9f", + "execution_count": null, + "id": "d07740ac", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -5841,7 +2186,7 @@ }, { "cell_type": "markdown", - "id": "845b4d8e", + "id": "fe40922b", "metadata": {}, "source": [ "We now instantiate our model and look at a summary." @@ -5849,42 +2194,10 @@ }, { "cell_type": "code", - "execution_count": 74, - "id": "66d0b710", - "metadata": { - "execution": {} - }, - "outputs": [ - { - "data": { - "text/plain": [ - "===================================================================================================================\n", - "Layer (type:depth-idx) Input Shape Output Shape Param #\n", - "===================================================================================================================\n", - "IMDBModel [25000, 10003] [25000] --\n", - "├─Linear: 1-1 [25000, 10003] [25000, 16] 160,064\n", - "├─ReLU: 1-2 [25000, 16] [25000, 16] --\n", - "├─Linear: 1-3 [25000, 16] [25000, 16] 272\n", - "├─ReLU: 1-4 [25000, 16] [25000, 16] --\n", - "├─Linear: 1-5 [25000, 16] [25000, 1] 17\n", - "===================================================================================================================\n", - "Total params: 160,353\n", - "Trainable params: 160,353\n", - "Non-trainable params: 0\n", - "Total mult-adds (G): 4.01\n", - "===================================================================================================================\n", - "Input size (MB): 1000.30\n", - "Forward/backward pass size (MB): 6.60\n", - "Params size (MB): 0.64\n", - "Estimated Total Size (MB): 1007.54\n", - "===================================================================================================================" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "6dac7a1a", + "metadata": {}, + "outputs": [], "source": [ "imdb_model = IMDBModel(imdb_test.tensors[0].size()[1])\n", "summary(imdb_model,\n", @@ -5896,7 +2209,7 @@ }, { "cell_type": "markdown", - "id": "c8bdad40", + "id": "4df7ce47", "metadata": {}, "source": [ "We’ll again use\n", @@ -5913,11 +2226,9 @@ }, { "cell_type": "code", - "execution_count": 75, - "id": "9df8b4cf", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "b8368753", + "metadata": {}, "outputs": [], "source": [ "imdb_optimizer = RMSprop(imdb_model.parameters(), lr=0.001)\n", @@ -5928,7 +2239,7 @@ }, { "cell_type": "markdown", - "id": "95cc3b4f", + "id": "e1b05702", "metadata": {}, "source": [ "Having loaded the datasets into a data module\n", @@ -5938,496 +2249,10 @@ }, { "cell_type": "code", - "execution_count": 76, - "id": "73684c66", - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: True\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "\n", - " | Name | Type | Params\n", - "--------------------------------------------\n", - "0 | model | IMDBModel | 160 K \n", - "1 | loss | BCEWithLogitsLoss | 0 \n", - "--------------------------------------------\n", - "160 K Trainable params\n", - "0 Non-trainable params\n", - "160 K Total params\n", - "0.641 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "795333e5d7b84fc6812fcf3dd6391bfa", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1_Ch10/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py:280: PossibleUserWarning: The number of training batches (45) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n", - " rank_zero_warn(\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "7c1ce82c29a644f68f5706783e08927a", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b1ac59bc49094a708bfa29f4fc194a71", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "21a870683e2a4e0ebb2b86c8c9afeb53", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - 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"model_id": "9ef5b02f482344818b2aeb04cc8ff5bf", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "2ed0de87ff334b7396d54733b8cb942c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "710efb8aace342a48ef6148920857667", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "491665fa6ec546cc87dfe5f3ad1858f5", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "7c2c49de7df44192b0085998f4ba1162", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ba942bfe8b80418290e2a2d43417cfbf", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=30` reached.\n" - ] - } - ], + "execution_count": null, + "id": "e699820c", + "metadata": {}, + "outputs": [], "source": [ "imdb_logger = CSVLogger('logs', name='IMDB')\n", "imdb_trainer = Trainer(deterministic=True,\n", @@ -6440,7 +2265,7 @@ }, { "cell_type": "markdown", - "id": "f2e03c88", + "id": "f22f6d2a", "metadata": {}, "source": [ "Evaluating the test error yields roughly 86% accuracy." @@ -6448,50 +2273,12 @@ }, { "cell_type": "code", - "execution_count": 77, - "id": "01c6e5ff", + "execution_count": null, + "id": "1aa09d1d", "metadata": { - "execution": {}, "lines_to_next_cell": 2 }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e7e77fe03abe4f2bbc3e182d32ec5074", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " Test metric DataLoader 0\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " test_accuracy 0.8492799997329712\n", - " test_loss 1.2849037647247314\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" - ] - }, - { - "data": { - "text/plain": [ - "[{'test_loss': 1.2849037647247314, 'test_accuracy': 0.8492799997329712}]" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "test_results = imdb_trainer.test(imdb_module, datamodule=imdb_dm)\n", "test_results" @@ -6499,7 +2286,7 @@ }, { "cell_type": "markdown", - "id": "c1976042", + "id": "434c482c", "metadata": {}, "source": [ "### Comparison to Lasso\n", @@ -6511,11 +2298,9 @@ }, { "cell_type": "code", - "execution_count": 78, - "id": "4d5b9d1d", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "6ca0f24a", + "metadata": {}, "outputs": [], "source": [ "((X_train, Y_train),\n", @@ -6527,7 +2312,7 @@ }, { "cell_type": "markdown", - "id": "b78eb64e", + "id": "0912454e", "metadata": {}, "source": [ "Similar to what we did in\n", @@ -6537,10 +2322,9 @@ }, { "cell_type": "code", - "execution_count": 79, - "id": "e2a88e57", + "execution_count": null, + "id": "24fa47c1", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -6552,7 +2336,7 @@ }, { "cell_type": "markdown", - "id": "81380bee", + "id": "40b6170b", "metadata": {}, "source": [ "With `LogisticRegression()` the regularization parameter\n", @@ -6563,10 +2347,9 @@ }, { "cell_type": "code", - "execution_count": 80, - "id": "9a3cf7a3", + "execution_count": null, + "id": "ae903c6b", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -6580,7 +2363,7 @@ }, { "cell_type": "markdown", - "id": "9ee15d44", + "id": "a5e5131f", "metadata": {}, "source": [ "The path of 50 values takes approximately 40 seconds to run." @@ -6588,11 +2371,9 @@ }, { "cell_type": "code", - "execution_count": 81, - "id": "b46f02c2", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "60e7c2ef", + "metadata": {}, "outputs": [], "source": [ "coefs = []\n", @@ -6607,7 +2388,7 @@ }, { "cell_type": "markdown", - "id": "eb3e3871", + "id": "cc471dac", "metadata": {}, "source": [ "The coefficient and intercepts have an extraneous dimension which can be removed\n", @@ -6616,10 +2397,9 @@ }, { "cell_type": "code", - "execution_count": 82, - "id": "e5fb6afa", + "execution_count": null, + "id": "f83501b9", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -6630,7 +2410,7 @@ }, { "cell_type": "markdown", - "id": "f66fa37c", + "id": "d3c2d1df", "metadata": {}, "source": [ "We’ll now make a plot to compare our neural network results with the\n", @@ -6639,10 +2419,9 @@ }, { "cell_type": "code", - "execution_count": 83, - "id": "cad28f1a", + "execution_count": null, + "id": "c52d5c7a", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -6673,7 +2452,7 @@ }, { "cell_type": "markdown", - "id": "c58ff7cb", + "id": "c061eec6", "metadata": {}, "source": [ "Notice the use of `%%capture`, which suppresses the displaying of the partially completed figure. This is useful\n", @@ -6683,25 +2462,12 @@ }, { "cell_type": "code", - "execution_count": 84, - "id": "a66ecdd8", + "execution_count": null, + "id": "4e92f90b", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "execution_count": 84, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "imdb_results = pd.read_csv(imdb_logger.experiment.metrics_file_path)\n", "summary_plot(imdb_results,\n", @@ -6721,7 +2487,7 @@ }, { "cell_type": "markdown", - "id": "fa1e2550", + "id": "3aac138f", "metadata": {}, "source": [ "From the graphs we see that the accuracy of the lasso logistic regression peaks at about $0.88$, as it does for the neural network.\n", @@ -6731,10 +2497,9 @@ }, { "cell_type": "code", - "execution_count": 85, - "id": "62440c1c", + "execution_count": null, + "id": "3d274eb0", "metadata": { - "execution": {}, "lines_to_next_cell": 2 }, "outputs": [], @@ -6749,7 +2514,7 @@ }, { "cell_type": "markdown", - "id": "fe67520f", + "id": "91fc3a79", "metadata": {}, "source": [ "## Recurrent Neural Networks\n", @@ -6759,7 +2524,7 @@ }, { "cell_type": "markdown", - "id": "a9bcdd2a", + "id": "7f4fd4c1", "metadata": {}, "source": [ "### Sequential Models for Document Classification\n", @@ -6778,11 +2543,9 @@ }, { "cell_type": "code", - "execution_count": 86, - "id": "c73d6e28", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "ad5fcbd0", + "metadata": {}, "outputs": [], "source": [ "imdb_seq_dm = SimpleDataModule(imdb_seq_train,\n", @@ -6795,7 +2558,7 @@ }, { "cell_type": "markdown", - "id": "1edd3143", + "id": "9e1161e9", "metadata": {}, "source": [ "The first layer of the RNN is an embedding layer of size 32, which will be\n", @@ -6812,7 +2575,7 @@ }, { "cell_type": "markdown", - "id": "b73e08ab", + "id": "4098afce", "metadata": {}, "source": [ "The second layer is an LSTM with 32 units, and the output\n", @@ -6823,10 +2586,9 @@ }, { "cell_type": "code", - "execution_count": 87, - "id": "cc9bbd00", + "execution_count": null, + "id": "329251bb", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -6846,7 +2608,7 @@ }, { "cell_type": "markdown", - "id": "5e9f28dd", + "id": "dbca8d65", "metadata": {}, "source": [ "We instantiate and take a look at the summary of the model, using the\n", @@ -6855,40 +2617,10 @@ }, { "cell_type": "code", - "execution_count": 88, - "id": "5c9ffb46", - "metadata": { - "execution": {} - }, - "outputs": [ - { - "data": { - "text/plain": [ - "===================================================================================================================\n", - "Layer (type:depth-idx) Input Shape Output Shape Param #\n", - "===================================================================================================================\n", - "LSTMModel [10, 500] [10] --\n", - "├─Embedding: 1-1 [10, 500] [10, 500, 32] 320,096\n", - "├─LSTM: 1-2 [10, 500, 32] [10, 500, 32] 8,448\n", - "├─Linear: 1-3 [10, 32] [10, 1] 33\n", - "===================================================================================================================\n", - "Total params: 328,577\n", - "Trainable params: 328,577\n", - "Non-trainable params: 0\n", - "Total mult-adds (M): 45.44\n", - "===================================================================================================================\n", - "Input size (MB): 50.00\n", - "Forward/backward pass size (MB): 2.56\n", - "Params size (MB): 1.31\n", - "Estimated Total Size (MB): 53.87\n", - "===================================================================================================================" - ] - }, - "execution_count": 88, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "ed65e386", + "metadata": {}, + "outputs": [], "source": [ "lstm_model = LSTMModel(X_test.shape[-1])\n", "summary(lstm_model,\n", @@ -6900,7 +2632,7 @@ }, { "cell_type": "markdown", - "id": "5087ff10", + "id": "63c49a33", "metadata": {}, "source": [ "The 10,003 is suppressed in the summary, but we see it in the\n", @@ -6909,11 +2641,9 @@ }, { "cell_type": "code", - "execution_count": 89, - "id": "a2d6ddfd", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "04934941", + "metadata": {}, "outputs": [], "source": [ "lstm_module = SimpleModule.binary_classification(lstm_model)\n", @@ -6922,349 +2652,12 @@ }, { "cell_type": "code", - "execution_count": 90, - "id": "1d76f970", + "execution_count": null, + "id": "25dc7682", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: True\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "\n", - " | Name | Type | Params\n", - "--------------------------------------------\n", - "0 | model | LSTMModel | 328 K \n", - "1 | loss | BCEWithLogitsLoss | 0 \n", - "--------------------------------------------\n", - "328 K Trainable params\n", - "0 Non-trainable params\n", - "328 K Total params\n", - "1.314 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "378b19c7eac744308f7e66510ed91e53", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ef5ef897fb0f445d8bdbb9603ab9fe1f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a366613bbed148f99faf7c3048fd5342", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "33c8cd8f47a44e5a86e58f83612915ab", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "44ba1553a3f54642b05ce7524df82025", - 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"Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "652b9712efa04ab2a0444464fb7aef43", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "8a69f0865eec43b5bab4d01c62ccc2dc", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "56fd72140fdf48fe87011e828715d038", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "aaad6e2fd6ff44aa9e7842830f2c31c5", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3eee2abff98b4e1080d1ade699034ec0", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1d18c86360cd4e32b69f20bb667f41ad", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e100cb13de18446f85a7d9864b04fbbf", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b22c18caa3c945ec9b5ea2427c8b76ad", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a3282157e73a4b6ba710cd530f2911e6", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "56a98588414e4d21a98f337a9121e943", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=20` reached.\n" - ] - } - ], + "outputs": [], "source": [ "lstm_trainer = Trainer(deterministic=True,\n", " max_epochs=20,\n", @@ -7276,7 +2669,7 @@ }, { "cell_type": "markdown", - "id": "1cbf1b42", + "id": "d5f6f6b4", "metadata": {}, "source": [ "The rest is now similar to other networks we have fit. We\n", @@ -7285,56 +2678,17 @@ }, { "cell_type": "code", - "execution_count": 91, - "id": "d8a60d35", - "metadata": { - "execution": {} - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "295ea7acd6904c2bac76f95a99e0e77e", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " Test metric DataLoader 0\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " test_accuracy 0.8549200296401978\n", - " test_loss 0.7031683325767517\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" - ] - }, - { - "data": { - "text/plain": [ - "[{'test_loss': 0.7031683325767517, 'test_accuracy': 0.8549200296401978}]" - ] - }, - "execution_count": 91, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "08004524", + "metadata": {}, + "outputs": [], "source": [ "lstm_trainer.test(lstm_module, datamodule=imdb_seq_dm)" ] }, { "cell_type": "markdown", - "id": "deca21cd", + "id": "d19c9ff7", "metadata": {}, "source": [ "We once again show the learning progress, followed by cleanup." @@ -7342,34 +2696,12 @@ }, { "cell_type": "code", - "execution_count": 92, - "id": "65d7276c", + "execution_count": null, + "id": "7be01434", "metadata": { - "execution": {}, "lines_to_next_cell": 2 }, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.5, 1.0)" - ] - }, - "execution_count": 92, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "lstm_results = pd.read_csv(lstm_logger.experiment.metrics_file_path)\n", "fig, ax = subplots(1, 1, figsize=(6, 6))\n", @@ -7384,10 +2716,9 @@ }, { "cell_type": "code", - "execution_count": 93, - "id": "c6f2d6c4", + "execution_count": null, + "id": "a8acb7e4", "metadata": { - "execution": {}, "lines_to_next_cell": 2 }, "outputs": [], @@ -7402,7 +2733,7 @@ }, { "cell_type": "markdown", - "id": "f920c659", + "id": "52540dc2", "metadata": {}, "source": [ "### Time Series Prediction\n", @@ -7413,11 +2744,9 @@ }, { "cell_type": "code", - "execution_count": 94, - "id": "f3e17682", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "5df58c36", + "metadata": {}, "outputs": [], "source": [ "NYSE = load_data('NYSE')\n", @@ -7431,7 +2760,7 @@ }, { "cell_type": "markdown", - "id": "e621c2eb", + "id": "f3655cfe", "metadata": {}, "source": [ "Next we set up the lagged versions of the data, dropping\n", @@ -7440,11 +2769,9 @@ }, { "cell_type": "code", - "execution_count": 95, - "id": "78707eda", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "6688e911", + "metadata": {}, "outputs": [], "source": [ "for lag in range(1, 6):\n", @@ -7458,7 +2785,7 @@ }, { "cell_type": "markdown", - "id": "bb83e654", + "id": "2146e793", "metadata": {}, "source": [ "Finally, we extract the response, training indicator, and drop the current day’s `DJ_return` and\n", @@ -7467,28 +2794,12 @@ }, { "cell_type": "code", - "execution_count": 96, - "id": "4d894824", + "execution_count": null, + "id": "7ca8735a", "metadata": { - "execution": {}, "lines_to_next_cell": 2 }, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['DJ_return_1', 'log_volume_1', 'log_volatility_1', 'DJ_return_2',\n", - " 'log_volume_2', 'log_volatility_2', 'DJ_return_3', 'log_volume_3',\n", - " 'log_volatility_3', 'DJ_return_4', 'log_volume_4', 'log_volatility_4',\n", - " 'DJ_return_5', 'log_volume_5', 'log_volatility_5'],\n", - " dtype='object')" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "Y, train = X['log_volume'], X['train']\n", "X = X.drop(columns=['train'] + cols)\n", @@ -7497,7 +2808,7 @@ }, { "cell_type": "markdown", - "id": "f1889769", + "id": "109fae4f", "metadata": {}, "source": [ "We first fit a simple linear model and compute the $R^2$ on the test data using\n", @@ -7506,23 +2817,10 @@ }, { "cell_type": "code", - "execution_count": 97, - "id": "4d7f5ce0", - "metadata": { - "execution": {} - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.4128912938562521" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "f921d473", + "metadata": {}, + "outputs": [], "source": [ "M = LinearRegression()\n", "M.fit(X[train], Y[train])\n", @@ -7531,7 +2829,7 @@ }, { "cell_type": "markdown", - "id": "02c7294f", + "id": "dbfd08c9", "metadata": {}, "source": [ "We refit this model, including the factor variable `day_of_week`.\n", @@ -7541,10 +2839,9 @@ }, { "cell_type": "code", - "execution_count": 98, - "id": "a6b371bb", + "execution_count": null, + "id": "3002d191", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -7556,7 +2853,7 @@ }, { "cell_type": "markdown", - "id": "92fda2e1", + "id": "8962fc26", "metadata": {}, "source": [ " Note that we do not have\n", @@ -7566,24 +2863,12 @@ }, { "cell_type": "code", - "execution_count": 99, - "id": "a2a8cc85", + "execution_count": null, + "id": "dc922a73", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.45633025715446285" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "M.fit(X_day[train], Y[train])\n", "M.score(X_day[~train], Y[~train])" @@ -7591,7 +2876,7 @@ }, { "cell_type": "markdown", - "id": "68a22c64", + "id": "188ca1af", "metadata": {}, "source": [ "This model achieves an $R^2$ of about 46%." @@ -7599,7 +2884,7 @@ }, { "cell_type": "markdown", - "id": "1cca42e4", + "id": "120352d5", "metadata": {}, "source": [ "To fit the RNN, we must reshape the data, as it will expect 5 lagged\n", @@ -7619,28 +2904,12 @@ }, { "cell_type": "code", - "execution_count": 100, - "id": "8ee6e6a3", + "execution_count": null, + "id": "4794d659", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['DJ_return_5', 'log_volume_5', 'log_volatility_5', 'DJ_return_4',\n", - " 'log_volume_4', 'log_volatility_4', 'DJ_return_3', 'log_volume_3',\n", - " 'log_volatility_3', 'DJ_return_2', 'log_volume_2', 'log_volatility_2',\n", - " 'DJ_return_1', 'log_volume_1', 'log_volatility_1'],\n", - " dtype='object')" - ] - }, - "execution_count": 100, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ordered_cols = []\n", "for lag in range(5,0,-1):\n", @@ -7652,7 +2921,7 @@ }, { "cell_type": "markdown", - "id": "a1f41334", + "id": "ce137e7d", "metadata": {}, "source": [ "We now reshape the data." @@ -7660,24 +2929,12 @@ }, { "cell_type": "code", - "execution_count": 101, - "id": "d35ceb54", + "execution_count": null, + "id": "e4b102d9", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "text/plain": [ - "(6046, 5, 3)" - ] - }, - "execution_count": 101, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "X_rnn = X.to_numpy().reshape((-1,5,3))\n", "X_rnn.shape" @@ -7685,7 +2942,7 @@ }, { "cell_type": "markdown", - "id": "11137e1d", + "id": "91d78412", "metadata": {}, "source": [ "By specifying the first size as -1, `numpy.reshape()` deduces its size based on the remaining arguments.\n", @@ -7699,11 +2956,9 @@ }, { "cell_type": "code", - "execution_count": 102, - "id": "9e3dc6d5", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "c6c636ec", + "metadata": {}, "outputs": [], "source": [ "class NYSEModel(nn.Module):\n", @@ -7723,7 +2978,7 @@ }, { "cell_type": "markdown", - "id": "1fd566f7", + "id": "965be876", "metadata": {}, "source": [ "We fit the model in a similar fashion to previous networks. We\n", @@ -7738,11 +2993,9 @@ }, { "cell_type": "code", - "execution_count": 103, - "id": "df5e5ab6", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "38b554ed", + "metadata": {}, "outputs": [], "source": [ "datasets = []\n", @@ -7755,7 +3008,7 @@ }, { "cell_type": "markdown", - "id": "16565d83", + "id": "52b079dc", "metadata": {}, "source": [ "Following our usual pattern, we inspect the summary." @@ -7763,41 +3016,12 @@ }, { "cell_type": "code", - "execution_count": 104, - "id": "d7f49bec", + "execution_count": null, + "id": "8355bc29", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "text/plain": [ - "===================================================================================================================\n", - "Layer (type:depth-idx) Input Shape Output Shape Param #\n", - "===================================================================================================================\n", - "NYSEModel [1770, 5, 3] [1770] --\n", - "├─RNN: 1-1 [1770, 5, 3] [1770, 5, 12] 204\n", - "├─Dropout: 1-2 [1770, 12] [1770, 12] --\n", - "├─Linear: 1-3 [1770, 12] [1770, 1] 13\n", - "===================================================================================================================\n", - "Total params: 217\n", - "Trainable params: 217\n", - "Non-trainable params: 0\n", - "Total mult-adds (M): 1.83\n", - "===================================================================================================================\n", - "Input size (MB): 0.11\n", - "Forward/backward pass size (MB): 0.86\n", - "Params size (MB): 0.00\n", - "Estimated Total Size (MB): 0.97\n", - "===================================================================================================================" - ] - }, - "execution_count": 104, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "summary(nyse_model,\n", " input_data=X_rnn_t,\n", @@ -7808,7 +3032,7 @@ }, { "cell_type": "markdown", - "id": "ad57780a", + "id": "08faf5d9", "metadata": {}, "source": [ "We again put the two datasets into a data module, with a\n", @@ -7817,10 +3041,9 @@ }, { "cell_type": "code", - "execution_count": 105, - "id": "ea7ce0f4", + "execution_count": null, + "id": "f203fefe", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -7834,7 +3057,7 @@ }, { "cell_type": "markdown", - "id": "d0d60c8b", + "id": "a00cb73e", "metadata": {}, "source": [ "We run some data through our model to be sure the sizes match up correctly." @@ -7842,22 +3065,10 @@ }, { "cell_type": "code", - "execution_count": 106, - "id": "ccd77738", - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([64]) torch.Size([64])\n", - "torch.Size([64]) torch.Size([64])\n", - "torch.Size([64]) torch.Size([64])\n" - ] - } - ], + "execution_count": null, + "id": "f230ccef", + "metadata": {}, + "outputs": [], "source": [ "for idx, (x, y) in enumerate(nyse_dm.train_dataloader()):\n", " out = nyse_model(x)\n", @@ -7868,7 +3079,7 @@ }, { "cell_type": "markdown", - "id": "c9397b7f", + "id": "470725a3", "metadata": {}, "source": [ "We follow our previous example for setting up a trainer for a\n", @@ -7878,11 +3089,9 @@ }, { "cell_type": "code", - "execution_count": 107, - "id": "96e04e3f", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "917a072c", + "metadata": {}, "outputs": [], "source": [ "nyse_optimizer = RMSprop(nyse_model.parameters(),\n", @@ -7894,7 +3103,7 @@ }, { "cell_type": "markdown", - "id": "9d7ef6fc", + "id": "688a11e1", "metadata": {}, "source": [ "Fitting the model should by now be familiar.\n", @@ -7903,2905 +3112,12 @@ }, { "cell_type": "code", - "execution_count": 108, - "id": "fc6ba2ca", + "execution_count": null, + "id": "e4c0d50b", "metadata": { - "execution": {}, "lines_to_next_cell": 2 }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: True\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "\n", - " | Name | Type | Params\n", - "------------------------------------\n", - "0 | model | NYSEModel | 217 \n", - "1 | loss | MSELoss | 0 \n", - 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" test_loss 0.6186699867248535\n", - " test_r2 0.4128519892692566\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" - ] - }, - { - "data": { - "text/plain": [ - "[{'test_loss': 0.6186699867248535, 'test_r2': 0.4128519892692566}]" - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "nyse_trainer = Trainer(deterministic=True,\n", " max_epochs=200,\n", @@ -10814,7 +3130,7 @@ }, { "cell_type": "markdown", - "id": "fc55d4f9", + "id": "189a6503", "metadata": {}, "source": [ "We could also fit a model without the `nn.RNN()` layer by just\n", @@ -10830,11 +3146,9 @@ }, { "cell_type": "code", - "execution_count": 109, - "id": "c60876d7", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "d26fc010", + "metadata": {}, "outputs": [], "source": [ "datasets = []\n", @@ -10848,7 +3162,7 @@ }, { "cell_type": "markdown", - "id": "c7fc165f", + "id": "03d4ff5a", "metadata": {}, "source": [ "Creating a data module follows a familiar pattern." @@ -10856,11 +3170,9 @@ }, { "cell_type": "code", - "execution_count": 110, - "id": "282f7e1c", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "459dab5c", + "metadata": {}, "outputs": [], "source": [ "day_dm = SimpleDataModule(day_train,\n", @@ -10872,7 +3184,7 @@ }, { "cell_type": "markdown", - "id": "310271e1", + "id": "74b773a3", "metadata": {}, "source": [ "We build a `NonLinearARModel()` that takes as input the 20 features and a hidden layer with 32 units. The remaining steps are familiar." @@ -10880,11 +3192,9 @@ }, { "cell_type": "code", - "execution_count": 111, - "id": "038fea74", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "df628a9a", + "metadata": {}, "outputs": [], "source": [ "class NonLinearARModel(nn.Module):\n", @@ -10901,11 +3211,9 @@ }, { "cell_type": "code", - "execution_count": 112, - "id": "68d309b2", - "metadata": { - "execution": {} - }, + "execution_count": null, + "id": "7a8fae94", + "metadata": {}, "outputs": [], "source": [ "nl_model = NonLinearARModel()\n", @@ -10918,7 +3226,7 @@ }, { "cell_type": "markdown", - "id": "998d317c", + "id": "4a2caf04", "metadata": {}, "source": [ "We continue with the usual training steps, fit the model,\n", @@ -10927,385 +3235,12 @@ }, { "cell_type": "code", - "execution_count": 113, - "id": "9fc113a1", + "execution_count": null, + "id": "4f2bf248", "metadata": { - "execution": {}, "lines_to_next_cell": 0 }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - 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