Skip to content

GitLab

"vscode:/vscode.git/clone" did not exist on "be31bd4779ac3073e005a2440a5384f5f19b1f0b"
Unverified Commit f1dbf247 authored by Joseph Friedman's avatar Joseph Friedman Committed by GitHub
Browse files

removed optional input_shape

parent af41cc00
Show whitespace changes
Inline Side-by-side
Showing with 5 additions and 5 deletions
samples/core/tutorials/keras/overfit_and_underfit.ipynb
View file @ f1dbf247
...@@ -294,7 +294,7 @@ ...@@ -294,7 +294,7 @@
"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, 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(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",
...@@ -365,7 +365,7 @@ ...@@ -365,7 +365,7 @@
"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, 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(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 +438,7 @@ ...@@ -438,7 +438,7 @@
"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, 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(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 +606,7 @@ ...@@ -606,7 +606,7 @@
"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, input_shape=(NUM_WORDS,)),\n", " activation=tf.nn.relu),\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 +697,7 @@ ...@@ -697,7 +697,7 @@
"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, input_shape=(NUM_WORDS,)),\n", " keras.layers.Dense(16, activation=tf.nn.relu),\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",
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment