mirror of
https://github.com/donnemartin/data-science-ipython-notebooks.git
synced 2024-03-22 13:30:56 +08:00
Replacing deprecated initialiser function with the new one (#41)
This commit is contained in:
parent
62f06d25d7
commit
a238b141f3
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@ -230,7 +230,7 @@
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"outputs": [],
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"source": [
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"# Initializing the variables\n",
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"init = tf.initialize_all_variables()"
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"init = tf.global_variables_initializer()"
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]
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},
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{
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@ -337,7 +337,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.5+"
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"version": "2.7.12"
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}
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},
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"nbformat": 4,
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@ -213,7 +213,7 @@
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"outputs": [],
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"source": [
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"# Initializing the variables\n",
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"init = tf.initialize_all_variables()"
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"init = tf.global_variables_initializer()"
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]
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},
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{
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@ -313,7 +313,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.5+"
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"version": "2.7.12"
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}
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},
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"nbformat": 4,
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@ -165,7 +165,7 @@
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"outputs": [],
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"source": [
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"# Initializing the variables\n",
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"init = tf.initialize_all_variables()"
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"init = tf.global_variables_initializer()"
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]
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},
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{
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@ -231,21 +231,21 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 2",
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"language": "python",
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"name": "python3"
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"name": "python2"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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": "3.4.3"
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"pygments_lexer": "ipython2",
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"version": "2.7.12"
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}
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},
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"nbformat": 4,
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@ -236,7 +236,7 @@
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],
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"source": [
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"# Initializing the variables\n",
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"init = tf.initialize_all_variables()\n",
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"init = tf.global_variables_initializer()\n",
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"\n",
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"# Launch the graph\n",
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"with tf.Session() as sess:\n",
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@ -286,7 +286,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.5+"
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"version": "2.7.12"
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}
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},
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"nbformat": 4,
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@ -381,7 +381,7 @@
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" # This is a one-time operation which ensures the parameters get initialized as\n",
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" # we described in the graph: random weights for the matrix, zeros for the\n",
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" # biases. \n",
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" tf.initialize_all_variables().run()\n",
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" tf.global_variables_initializer().run()\n",
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" print 'Initialized'\n",
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" for step in xrange(num_steps):\n",
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" # Run the computations. We tell .run() that we want to run the optimizer,\n",
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@ -544,7 +544,7 @@
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"num_steps = 3001\n",
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"\n",
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"with tf.Session(graph=graph) as session:\n",
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" tf.initialize_all_variables().run()\n",
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" tf.global_variables_initializer().run()\n",
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" print \"Initialized\"\n",
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" for step in xrange(num_steps):\n",
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" # Pick an offset within the training data, which has been randomized.\n",
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@ -589,21 +589,21 @@
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"colab_default_view": {},
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"colab_views": {},
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 2",
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"language": "python",
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"name": "python3"
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"name": "python2"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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": "3.4.3"
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"pygments_lexer": "ipython2",
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"version": "2.7.12"
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}
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},
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"nbformat": 4,
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@ -412,7 +412,7 @@
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"num_steps = 1001\n",
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"\n",
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"with tf.Session(graph=graph) as session:\n",
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" tf.initialize_all_variables().run()\n",
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" tf.global_variables_initializer().run()\n",
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" print \"Initialized\"\n",
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" for step in xrange(num_steps):\n",
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" offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n",
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@ -467,21 +467,21 @@
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"colab_default_view": {},
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"colab_views": {},
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 2",
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"language": "python",
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"name": "python3"
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"name": "python2"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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": "3.4.3"
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"pygments_lexer": "ipython2",
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"version": "2.7.12"
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}
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},
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"nbformat": 4,
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@ -744,7 +744,7 @@
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"num_steps = 100001\n",
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"\n",
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"with tf.Session(graph=graph) as session:\n",
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" tf.initialize_all_variables().run()\n",
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" tf.global_variables_initializer().run()\n",
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" print \"Initialized\"\n",
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" average_loss = 0\n",
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" for step in xrange(num_steps):\n",
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@ -867,21 +867,21 @@
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"colab_default_view": {},
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"colab_views": {},
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 2",
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"language": "python",
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"name": "python3"
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"name": "python2"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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": "3.4.3"
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"pygments_lexer": "ipython2",
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"version": "2.7.12"
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}
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},
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"nbformat": 4,
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@ -928,7 +928,7 @@
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"summary_frequency = 100\n",
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"\n",
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"with tf.Session(graph=graph) as session:\n",
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" tf.initialize_all_variables().run()\n",
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" tf.global_variables_initializer().run()\n",
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" print 'Initialized'\n",
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" mean_loss = 0\n",
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" for step in xrange(num_steps):\n",
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@ -1042,21 +1042,21 @@
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"colab_default_view": {},
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"colab_views": {},
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 2",
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"language": "python",
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"name": "python3"
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"name": "python2"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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": "3.4.3"
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"pygments_lexer": "ipython2",
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"version": "2.7.12"
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}
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},
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"nbformat": 4,
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