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ModelZoo
ResNet50_tensorflow
Commits
1be8e32a
"sgl-kernel/git@developer.sourcefind.cn:change/sglang.git" did not exist on "006ead9dcbec360c0a656ee84d34446dc80c88c7"
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Commit
1be8e32a
authored
Jul 30, 2018
by
Mark Daoust
Committed by
GitHub
Jul 30, 2018
Browse files
It turns out `input_shape` is needed by `summary`
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f1dbf247
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samples/core/tutorials/keras/overfit_and_underfit.ipynb
samples/core/tutorials/keras/overfit_and_underfit.ipynb
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samples/core/tutorials/keras/overfit_and_underfit.ipynb
View file @
1be8e32a
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@@ -294,7 +294,8 @@
...
@@ -294,7 +294,8 @@
"cell_type": "code",
"cell_type": "code",
"source": [
"source": [
"baseline_model = keras.Sequential([\n",
"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(16, activation=tf.nn.relu),\n",
" keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
" keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
"])\n",
"])\n",
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@@ -365,7 +366,7 @@
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@@ -365,7 +366,7 @@
"cell_type": "code",
"cell_type": "code",
"source": [
"source": [
"smaller_model = keras.Sequential([\n",
"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(4, activation=tf.nn.relu),\n",
" keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
" keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
"])\n",
"])\n",
...
@@ -438,7 +439,7 @@
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@@ -438,7 +439,7 @@
"cell_type": "code",
"cell_type": "code",
"source": [
"source": [
"bigger_model = keras.models.Sequential([\n",
"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(512, activation=tf.nn.relu),\n",
" keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
" keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
"])\n",
"])\n",
...
@@ -606,7 +607,7 @@
...
@@ -606,7 +607,7 @@
"source": [
"source": [
"l2_model = keras.models.Sequential([\n",
"l2_model = keras.models.Sequential([\n",
" keras.layers.Dense(16, kernel_regularizer=keras.regularizers.l2(0.001),\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",
" keras.layers.Dense(16, kernel_regularizer=keras.regularizers.l2(0.001),\n",
" activation=tf.nn.relu),\n",
" activation=tf.nn.relu),\n",
" keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
" keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
...
@@ -697,7 +698,7 @@
...
@@ -697,7 +698,7 @@
"cell_type": "code",
"cell_type": "code",
"source": [
"source": [
"dpt_model = keras.models.Sequential([\n",
"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.Dropout(0.5),\n",
" keras.layers.Dense(16, activation=tf.nn.relu),\n",
" keras.layers.Dense(16, activation=tf.nn.relu),\n",
" keras.layers.Dropout(0.5),\n",
" keras.layers.Dropout(0.5),\n",
...
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