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tf.variance_scaling_initializer로 변경

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rickiepark
2018-05-20 23:13:48 +09:00
parent 3ae60bc7c9
commit 5808bd3ae3
1 changed files with 7 additions and 7 deletions
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14
15_autoencoders.ipynb
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@@ -366,7 +366,7 @@
"\n", "\n",
"X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n", "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
"\n", "\n",
"he_init = tf.contrib.layers.variance_scaling_initializer() # He 초기화\n", "he_init = tf.variance_scaling_initializer() # He 초기화\n",
"#아래와 동일합니다:\n", "#아래와 동일합니다:\n",
"#he_init = lambda shape, dtype=tf.float32: tf.truncated_normal(shape, 0., stddev=np.sqrt(2/shape[0]))\n", "#he_init = lambda shape, dtype=tf.float32: tf.truncated_normal(shape, 0., stddev=np.sqrt(2/shape[0]))\n",
"l2_regularizer = tf.contrib.layers.l2_regularizer(l2_reg)\n", "l2_regularizer = tf.contrib.layers.l2_regularizer(l2_reg)\n",
@@ -532,7 +532,7 @@
"source": [ "source": [
"activation = tf.nn.elu\n", "activation = tf.nn.elu\n",
"regularizer = tf.contrib.layers.l2_regularizer(l2_reg)\n", "regularizer = tf.contrib.layers.l2_regularizer(l2_reg)\n",
"initializer = tf.contrib.layers.variance_scaling_initializer()\n", "initializer = tf.variance_scaling_initializer()\n",
"\n", "\n",
"X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n", "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
"\n", "\n",
@@ -680,7 +680,7 @@
" \n", " \n",
" my_dense_layer = partial(\n", " my_dense_layer = partial(\n",
" tf.layers.dense,\n", " tf.layers.dense,\n",
" kernel_initializer=tf.contrib.layers.variance_scaling_initializer(),\n", " kernel_initializer=tf.variance_scaling_initializer(),\n",
" kernel_regularizer=tf.contrib.layers.l2_regularizer(l2_reg))\n", " kernel_regularizer=tf.contrib.layers.l2_regularizer(l2_reg))\n",
"\n", "\n",
" hidden = my_dense_layer(X, n_neurons, activation=hidden_activation, name=\"hidden\")\n", " hidden = my_dense_layer(X, n_neurons, activation=hidden_activation, name=\"hidden\")\n",
@@ -823,7 +823,7 @@
"\n", "\n",
"activation = tf.nn.elu\n", "activation = tf.nn.elu\n",
"regularizer = tf.contrib.layers.l2_regularizer(l2_reg)\n", "regularizer = tf.contrib.layers.l2_regularizer(l2_reg)\n",
"initializer = tf.contrib.layers.variance_scaling_initializer()\n", "initializer = tf.variance_scaling_initializer()\n",
"\n", "\n",
"X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n", "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
"\n", "\n",
@@ -1119,7 +1119,7 @@
"\n", "\n",
"activation = tf.nn.elu\n", "activation = tf.nn.elu\n",
"regularizer = tf.contrib.layers.l2_regularizer(l2_reg)\n", "regularizer = tf.contrib.layers.l2_regularizer(l2_reg)\n",
"initializer = tf.contrib.layers.variance_scaling_initializer()\n", "initializer = tf.variance_scaling_initializer()\n",
"\n", "\n",
"X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n", "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
"y = tf.placeholder(tf.int32, shape=[None])\n", "y = tf.placeholder(tf.int32, shape=[None])\n",
@@ -1791,7 +1791,7 @@
"n_outputs = n_inputs\n", "n_outputs = n_inputs\n",
"learning_rate = 0.001\n", "learning_rate = 0.001\n",
"\n", "\n",
"initializer = tf.contrib.layers.variance_scaling_initializer()\n", "initializer = tf.variance_scaling_initializer()\n",
"\n", "\n",
"my_dense_layer = partial(\n", "my_dense_layer = partial(\n",
" tf.layers.dense,\n", " tf.layers.dense,\n",
@@ -1940,7 +1940,7 @@
"n_outputs = n_inputs\n", "n_outputs = n_inputs\n",
"learning_rate = 0.001\n", "learning_rate = 0.001\n",
"\n", "\n",
"initializer = tf.contrib.layers.variance_scaling_initializer()\n", "initializer = tf.variance_scaling_initializer()\n",
"my_dense_layer = partial(\n", "my_dense_layer = partial(\n",
" tf.layers.dense,\n", " tf.layers.dense,\n",
" activation=tf.nn.elu,\n", " activation=tf.nn.elu,\n",