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Merge pull request #10 from bobarna/main

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Small updates to intro-teaser.ipynb
This commit is contained in:
Nils Thuerey
2021-12-03 12:54:51 +01:00
committed by GitHub
parent 8d12aac918 3a15ab71cb
commit 24130d50be
1 changed files with 4 additions and 4 deletions
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intro-teaser.ipynb
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@@ -134,7 +134,7 @@
"# Neural network\n", "# Neural network\n",
"act = tf.keras.layers.ReLU()\n", "act = tf.keras.layers.ReLU()\n",
"nn_sv = tf.keras.models.Sequential([\n", "nn_sv = tf.keras.models.Sequential([\n",
" tf.keras.layers.Dense(10, activation=act),\n", " tf.keras.layers.Dense(10, activation=act, input_shape=(1,)),\n",
" tf.keras.layers.Dense(10, activation=act),\n", " tf.keras.layers.Dense(10, activation=act),\n",
" tf.keras.layers.Dense(1,activation='linear')])" " tf.keras.layers.Dense(1,activation='linear')])"
] ]
@@ -173,7 +173,7 @@
"source": [ "source": [
"# Loss function\n", "# Loss function\n",
"loss_sv = tf.keras.losses.MeanSquaredError()\n", "loss_sv = tf.keras.losses.MeanSquaredError()\n",
"optimizer_sv = tf.keras.optimizers.Adam(lr=0.001)\n", "optimizer_sv = tf.keras.optimizers.Adam(learning_rate=0.001)\n",
"nn_sv.compile(optimizer=optimizer_sv, loss=loss_sv)\n", "nn_sv.compile(optimizer=optimizer_sv, loss=loss_sv)\n",
"\n", "\n",
"# Training\n", "# Training\n",
@@ -264,7 +264,7 @@
"\n", "\n",
"# Model\n", "# Model\n",
"nn_dp = tf.keras.models.Sequential([\n", "nn_dp = tf.keras.models.Sequential([\n",
" tf.keras.layers.Dense(10, activation=act),\n", " tf.keras.layers.Dense(10, activation=act, input_shape=(1,)),\n",
" tf.keras.layers.Dense(10, activation=act),\n", " tf.keras.layers.Dense(10, activation=act),\n",
" tf.keras.layers.Dense(1, activation='linear')])" " tf.keras.layers.Dense(1, activation='linear')])"
] ]
@@ -291,7 +291,7 @@
"def loss_dp(y_true, y_pred):\n", "def loss_dp(y_true, y_pred):\n",
" return mse(y_true,y_pred**2)\n", " return mse(y_true,y_pred**2)\n",
"\n", "\n",
"optimizer_dp = tf.keras.optimizers.Adam(lr=0.001)\n", "optimizer_dp = tf.keras.optimizers.Adam(learning_rate=0.001)\n",
"nn_dp.compile(optimizer=optimizer_dp, loss=loss_dp)" "nn_dp.compile(optimizer=optimizer_dp, loss=loss_dp)"
] ]
}, },