tf.contrib.layers.variance_scaling_initializer moved to tf.variance_scaling_initializer
parent
38c2ea79e4
commit
e05d4b36ac
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@ -574,15 +574,6 @@
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" plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"openai_cart_pole_rendering = False # don't try, just use the safe way?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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@ -783,7 +774,7 @@
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"n_inputs = 4 # == env.observation_space.shape[0]\n",
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"n_hidden = 4 # it's a simple task, we don't need more than this\n",
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"n_outputs = 1 # only outputs the probability of accelerating left\n",
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"initializer = tf.contrib.layers.variance_scaling_initializer()\n",
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"initializer = tf.variance_scaling_initializer()\n",
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"\n",
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"# 2. Build the neural network\n",
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"X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
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@ -883,7 +874,7 @@
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"\n",
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"learning_rate = 0.01\n",
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"\n",
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"initializer = tf.contrib.layers.variance_scaling_initializer()\n",
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"initializer = tf.variance_scaling_initializer()\n",
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"\n",
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"X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
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"y = tf.placeholder(tf.float32, shape=[None, n_outputs])\n",
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@ -1008,7 +999,7 @@
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"\n",
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"learning_rate = 0.01\n",
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"\n",
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"initializer = tf.contrib.layers.variance_scaling_initializer()\n",
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"initializer = tf.variance_scaling_initializer()\n",
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"\n",
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"X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
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"\n",
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@ -1481,7 +1472,7 @@
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"n_hidden = 512\n",
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"hidden_activation = tf.nn.relu\n",
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"n_outputs = env.action_space.n # 9 discrete actions are available\n",
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"initializer = tf.contrib.layers.variance_scaling_initializer()\n",
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"initializer = tf.variance_scaling_initializer()\n",
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"\n",
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"def q_network(X_state, name):\n",
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" prev_layer = X_state / 128.0 # scale pixel intensities to the [-1.0, 1.0] range.\n",
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@ -1924,7 +1915,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.3"
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"version": "3.5.2"
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},
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"nav_menu": {},
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"toc": {
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