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Unverified Commit 1be8e32a authored by Mark Daoust's avatar Mark Daoust Committed by GitHub
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It turns out `input_shape` is needed by `summary`

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samples/core/tutorials/keras/overfit_and_underfit.ipynb
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......@@ -294,7 +294,8 @@
"cell_type": "code",
"source": [
"baseline_model = keras.Sequential([\n",
" keras.layers.Dense(16, activation=tf.nn.relu),\n",
" # `input_shape` is only required here so that `.summary` works. \n"
" keras.layers.Dense(16, activation=tf.nn.relu, input_shape=(NUM_WORDS,)),\n",
" keras.layers.Dense(16, activation=tf.nn.relu),\n",
" keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
"])\n",
......@@ -365,7 +366,7 @@
"cell_type": "code",
"source": [
"smaller_model = keras.Sequential([\n",
" keras.layers.Dense(4, activation=tf.nn.relu),\n",
" keras.layers.Dense(4, activation=tf.nn.relu, input_shape=(NUM_WORDS,)),\n",
" keras.layers.Dense(4, activation=tf.nn.relu),\n",
" keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
"])\n",
......@@ -438,7 +439,7 @@
"cell_type": "code",
"source": [
"bigger_model = keras.models.Sequential([\n",
" keras.layers.Dense(512, activation=tf.nn.relu),\n",
" keras.layers.Dense(512, activation=tf.nn.relu, input_shape=(NUM_WORDS,)),\n",
" keras.layers.Dense(512, activation=tf.nn.relu),\n",
" keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
"])\n",
......@@ -606,7 +607,7 @@
"source": [
"l2_model = keras.models.Sequential([\n",
" keras.layers.Dense(16, kernel_regularizer=keras.regularizers.l2(0.001),\n",
" activation=tf.nn.relu),\n",
" activation=tf.nn.relu, input_shape=(NUM_WORDS,)),\n",
" keras.layers.Dense(16, kernel_regularizer=keras.regularizers.l2(0.001),\n",
" activation=tf.nn.relu),\n",
" keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
......@@ -697,7 +698,7 @@
"cell_type": "code",
"source": [
"dpt_model = keras.models.Sequential([\n",
" keras.layers.Dense(16, activation=tf.nn.relu),\n",
" keras.layers.Dense(16, activation=tf.nn.relu, input_shape=(NUM_WORDS,)),\n",
" keras.layers.Dropout(0.5),\n",
" keras.layers.Dense(16, activation=tf.nn.relu),\n",
" keras.layers.Dropout(0.5),\n",
......
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