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Use np.random.set_seed(42) and tf.set_random_seed(42) to make notebook's output constant, and simplify code in notebook 15

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This commit is contained in:
Aurélien Geron
2017-06-07 17:52:59 +02:00
parent 045150bd95
commit 74794da1de
3 changed files with 957 additions and 648 deletions
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20
12_distributed_tensorflow.ipynb
View File

@@ -55,11 +55,13 @@
"\n",
"# Common imports\n",
"import numpy as np\n",
"import numpy.random as rnd\n",
"import os\n",
"\n",
"# to make this notebook's output stable across runs\n",
"rnd.seed(42)\n",
"def reset_graph(seed=42):\n",
" tf.reset_default_graph()\n",
" tf.set_random_seed(seed)\n",
" np.random.seed(seed)\n",
"\n",
"# To plot pretty figures\n",
"%matplotlib inline\n",
@@ -201,7 +203,7 @@
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"reset_graph()\n",
"\n",
"with tf.device(\"/job:ps\"):\n",
" a = tf.Variable(1.0, name=\"a\")\n",
@@ -238,7 +240,7 @@
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"reset_graph()\n",
"\n",
"with tf.device(tf.train.replica_device_setter(\n",
" ps_tasks=2,\n",
@@ -280,7 +282,7 @@
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"reset_graph()\n",
"\n",
"test_csv = open(\"my_test.csv\", \"w\")\n",
"test_csv.write(\"x1, x2 , target\\n\")\n",
@@ -362,7 +364,7 @@
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"reset_graph()\n",
"\n",
"filename_queue = tf.FIFOQueue(capacity=10, dtypes=[tf.string], shapes=[()])\n",
"filename = tf.placeholder(tf.string)\n",
@@ -409,7 +411,7 @@
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"reset_graph()\n",
"\n",
"def read_and_push_instance(filename_queue, instance_queue):\n",
" reader = tf.TextLineReader(skip_header_lines=1)\n",
@@ -467,7 +469,7 @@
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"reset_graph()\n",
"\n",
"q = tf.FIFOQueue(capacity=10, dtypes=[tf.float32], shapes=[()])\n",
"v = tf.placeholder(tf.float32)\n",
@@ -515,7 +517,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 15,
"metadata": {
"collapsed": true,
"deletable": true,

262
14_recurrent_neural_networks.ipynb
View File

@@ -55,11 +55,13 @@
"\n",
"# Common imports\n",
"import numpy as np\n",
"import numpy.random as rnd\n",
"import os\n",
"\n",
"# to make this notebook's output stable across runs\n",
"rnd.seed(42)\n",
"def reset_graph(seed=42):\n",
" tf.reset_default_graph()\n",
" tf.set_random_seed(seed)\n",
" np.random.seed(seed)\n",
"\n",
"# To plot pretty figures\n",
"%matplotlib inline\n",
@@ -134,7 +136,7 @@
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"reset_graph()\n",
"\n",
"n_inputs = 3\n",
"n_neurons = 5\n",
@@ -205,7 +207,7 @@
"editable": true
},
"source": [
"## Using `rnn()`"
"## Using `static_rnn()`"
]
},
{
@@ -218,8 +220,6 @@
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"\n",
"n_inputs = 3\n",
"n_neurons = 5"
]
@@ -234,6 +234,8 @@
},
"outputs": [],
"source": [
"reset_graph()\n",
"\n",
"X0 = tf.placeholder(tf.float32, [None, n_inputs])\n",
"X1 = tf.placeholder(tf.float32, [None, n_inputs])\n",
"\n",
@@ -381,8 +383,6 @@
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"\n",
"n_steps = 2\n",
"n_inputs = 3\n",
"n_neurons = 5"
@@ -398,6 +398,8 @@
},
"outputs": [],
"source": [
"reset_graph()\n",
"\n",
"X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
"X_seqs = tf.unstack(tf.transpose(X, perm=[1, 0, 2]))\n",
"\n",
@@ -446,6 +448,17 @@
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(outputs_val)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -468,7 +481,7 @@
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 21,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -476,8 +489,6 @@
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"\n",
"n_steps = 2\n",
"n_inputs = 3\n",
"n_neurons = 5"
@@ -485,7 +496,7 @@
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 22,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -493,6 +504,8 @@
},
"outputs": [],
"source": [
"reset_graph()\n",
"\n",
"X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
"\n",
"basic_cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)\n",
@@ -501,7 +514,7 @@
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 23,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -514,7 +527,7 @@
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 24,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -531,12 +544,23 @@
"\n",
"with tf.Session() as sess:\n",
" init.run()\n",
" print(\"outputs =\", outputs.eval(feed_dict={X: X_batch}))"
" outputs_val = outputs.eval(feed_dict={X: X_batch})"
]
},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(outputs_val)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -559,7 +583,7 @@
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": 27,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -567,19 +591,19 @@
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"\n",
"n_steps = 2\n",
"n_inputs = 3\n",
"n_neurons = 5\n",
"\n",
"reset_graph()\n",
"\n",
"X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
"basic_cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": 28,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -594,7 +618,7 @@
},
{
"cell_type": "code",
"execution_count": 27,
"execution_count": 29,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -607,7 +631,7 @@
},
{
"cell_type": "code",
"execution_count": 28,
"execution_count": 30,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -627,7 +651,7 @@
},
{
"cell_type": "code",
"execution_count": 29,
"execution_count": 31,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -643,7 +667,7 @@
},
{
"cell_type": "code",
"execution_count": 30,
"execution_count": 32,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -656,7 +680,7 @@
},
{
"cell_type": "code",
"execution_count": 31,
"execution_count": 33,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -691,7 +715,7 @@
},
{
"cell_type": "code",
"execution_count": 32,
"execution_count": 34,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -699,7 +723,7 @@
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"reset_graph()\n",
"\n",
"n_steps = 28\n",
"n_inputs = 28\n",
@@ -728,7 +752,7 @@
},
{
"cell_type": "code",
"execution_count": 33,
"execution_count": 35,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -744,7 +768,7 @@
},
{
"cell_type": "code",
"execution_count": 34,
"execution_count": 36,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -779,7 +803,7 @@
},
{
"cell_type": "code",
"execution_count": 35,
"execution_count": 37,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -787,7 +811,7 @@
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"reset_graph()\n",
"\n",
"n_steps = 28\n",
"n_inputs = 28\n",
@@ -801,7 +825,7 @@
},
{
"cell_type": "code",
"execution_count": 36,
"execution_count": 38,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -821,7 +845,7 @@
},
{
"cell_type": "code",
"execution_count": 37,
"execution_count": 39,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -843,7 +867,7 @@
},
{
"cell_type": "code",
"execution_count": 38,
"execution_count": 40,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -878,7 +902,7 @@
},
{
"cell_type": "code",
"execution_count": 39,
"execution_count": 41,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -901,7 +925,7 @@
},
{
"cell_type": "code",
"execution_count": 40,
"execution_count": 42,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -938,7 +962,7 @@
},
{
"cell_type": "code",
"execution_count": 41,
"execution_count": 43,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -951,7 +975,7 @@
},
{
"cell_type": "code",
"execution_count": 42,
"execution_count": 44,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -984,7 +1008,7 @@
},
{
"cell_type": "code",
"execution_count": 43,
"execution_count": 45,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -992,7 +1016,7 @@
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"reset_graph()\n",
"\n",
"n_steps = 20\n",
"n_inputs = 1\n",
@@ -1018,7 +1042,7 @@
},
{
"cell_type": "code",
"execution_count": 44,
"execution_count": 46,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -1026,7 +1050,7 @@
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"reset_graph()\n",
"\n",
"n_steps = 20\n",
"n_inputs = 1\n",
@@ -1039,7 +1063,7 @@
},
{
"cell_type": "code",
"execution_count": 45,
"execution_count": 47,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -1054,7 +1078,7 @@
},
{
"cell_type": "code",
"execution_count": 46,
"execution_count": 48,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -1067,7 +1091,7 @@
},
{
"cell_type": "code",
"execution_count": 47,
"execution_count": 49,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -1086,7 +1110,7 @@
},
{
"cell_type": "code",
"execution_count": 48,
"execution_count": 50,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -1099,7 +1123,7 @@
},
{
"cell_type": "code",
"execution_count": 49,
"execution_count": 51,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -1124,7 +1148,7 @@
},
{
"cell_type": "code",
"execution_count": 50,
"execution_count": 52,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -1141,7 +1165,7 @@
},
{
"cell_type": "code",
"execution_count": 51,
"execution_count": 53,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -1154,7 +1178,7 @@
},
{
"cell_type": "code",
"execution_count": 52,
"execution_count": 54,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -1185,7 +1209,7 @@
},
{
"cell_type": "code",
"execution_count": 53,
"execution_count": 55,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -1193,7 +1217,7 @@
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"reset_graph()\n",
"\n",
"n_steps = 20\n",
"n_inputs = 1\n",
@@ -1205,7 +1229,7 @@
},
{
"cell_type": "code",
"execution_count": 54,
"execution_count": 56,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -1219,7 +1243,7 @@
},
{
"cell_type": "code",
"execution_count": 55,
"execution_count": 57,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -1233,7 +1257,7 @@
},
{
"cell_type": "code",
"execution_count": 56,
"execution_count": 58,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -1248,7 +1272,7 @@
},
{
"cell_type": "code",
"execution_count": 57,
"execution_count": 59,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -1266,7 +1290,7 @@
},
{
"cell_type": "code",
"execution_count": 58,
"execution_count": 60,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -1294,7 +1318,7 @@
},
{
"cell_type": "code",
"execution_count": 59,
"execution_count": 61,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -1307,7 +1331,7 @@
},
{
"cell_type": "code",
"execution_count": 60,
"execution_count": 62,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -1337,7 +1361,7 @@
},
{
"cell_type": "code",
"execution_count": 61,
"execution_count": 63,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -1357,7 +1381,7 @@
},
{
"cell_type": "code",
"execution_count": 62,
"execution_count": 64,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -1375,7 +1399,7 @@
},
{
"cell_type": "code",
"execution_count": 63,
"execution_count": 65,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -1435,7 +1459,7 @@
},
{
"cell_type": "code",
"execution_count": 64,
"execution_count": 66,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -1443,7 +1467,7 @@
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"reset_graph()\n",
"\n",
"n_inputs = 2\n",
"n_steps = 5\n",
@@ -1453,7 +1477,7 @@
},
{
"cell_type": "code",
"execution_count": 65,
"execution_count": 67,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -1472,7 +1496,7 @@
},
{
"cell_type": "code",
"execution_count": 66,
"execution_count": 68,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -1485,7 +1509,7 @@
},
{
"cell_type": "code",
"execution_count": 67,
"execution_count": 69,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -1498,7 +1522,7 @@
},
{
"cell_type": "code",
"execution_count": 68,
"execution_count": 70,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -1513,7 +1537,7 @@
},
{
"cell_type": "code",
"execution_count": 69,
"execution_count": 71,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -1546,7 +1570,7 @@
},
{
"cell_type": "code",
"execution_count": 70,
"execution_count": 72,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -1573,7 +1597,7 @@
},
{
"cell_type": "code",
"execution_count": 71,
"execution_count": 73,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -1603,7 +1627,7 @@
},
{
"cell_type": "code",
"execution_count": 72,
"execution_count": 74,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -1611,7 +1635,7 @@
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"reset_graph()\n",
"\n",
"n_inputs = 5\n",
"n_steps = 20\n",
@@ -1622,7 +1646,7 @@
},
{
"cell_type": "code",
"execution_count": 73,
"execution_count": 75,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -1639,7 +1663,7 @@
},
{
"cell_type": "code",
"execution_count": 74,
"execution_count": 76,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -1652,7 +1676,7 @@
},
{
"cell_type": "code",
"execution_count": 75,
"execution_count": 77,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -1678,7 +1702,7 @@
},
{
"cell_type": "code",
"execution_count": 76,
"execution_count": 78,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -1686,7 +1710,7 @@
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"reset_graph()\n",
"\n",
"n_inputs = 1\n",
"n_neurons = 100\n",
@@ -1700,7 +1724,7 @@
},
{
"cell_type": "code",
"execution_count": 77,
"execution_count": 79,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -1720,7 +1744,7 @@
},
{
"cell_type": "code",
"execution_count": 78,
"execution_count": 80,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -1754,7 +1778,7 @@
},
{
"cell_type": "code",
"execution_count": 79,
"execution_count": 81,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -1788,7 +1812,7 @@
},
{
"cell_type": "code",
"execution_count": 80,
"execution_count": 82,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -1796,7 +1820,7 @@
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"reset_graph()\n",
"\n",
"n_inputs = 1\n",
"n_neurons = 100\n",
@@ -1828,7 +1852,7 @@
},
{
"cell_type": "code",
"execution_count": 81,
"execution_count": 83,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -1874,7 +1898,7 @@
},
{
"cell_type": "code",
"execution_count": 82,
"execution_count": 84,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -1882,7 +1906,7 @@
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"reset_graph()\n",
"\n",
"import sys\n",
"training = True # in a script, this would be (sys.argv[-1] == \"train\") instead\n",
@@ -1934,7 +1958,7 @@
},
{
"cell_type": "code",
"execution_count": 83,
"execution_count": 85,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -1942,12 +1966,14 @@
},
"outputs": [],
"source": [
"reset_graph()\n",
"\n",
"lstm_cell = tf.contrib.rnn.BasicLSTMCell(num_units=n_neurons)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"execution_count": 86,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -1955,8 +1981,6 @@
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"\n",
"n_steps = 28\n",
"n_inputs = 28\n",
"n_neurons = 150\n",
@@ -1986,7 +2010,7 @@
},
{
"cell_type": "code",
"execution_count": 85,
"execution_count": 87,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -1999,7 +2023,7 @@
},
{
"cell_type": "code",
"execution_count": 86,
"execution_count": 88,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -2012,7 +2036,7 @@
},
{
"cell_type": "code",
"execution_count": 87,
"execution_count": 89,
"metadata": {
"collapsed": false,
"deletable": true,
@@ -2038,7 +2062,7 @@
},
{
"cell_type": "code",
"execution_count": 88,
"execution_count": 90,
"metadata": {
"collapsed": true,
"deletable": true,
@@ -2051,7 +2075,7 @@
},
{
"cell_type": "code",
"execution_count": 89,
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@@ -2094,7 +2118,7 @@
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@@ -2137,7 +2161,7 @@
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@@ -2150,7 +2174,7 @@
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@@ -2173,7 +2197,7 @@
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@@ -2193,7 +2217,7 @@
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@@ -2206,7 +2230,7 @@
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@@ -2242,7 +2266,7 @@
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@@ -2280,7 +2304,7 @@
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@@ -2294,7 +2318,7 @@
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@@ -2307,7 +2331,7 @@
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@@ -2330,7 +2354,7 @@
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@@ -2356,7 +2380,7 @@
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{
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@@ -2364,7 +2388,7 @@
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"reset_graph()\n",
"\n",
"# Input data.\n",
"train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\n",
@@ -2373,7 +2397,7 @@
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{
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@@ -2391,7 +2415,7 @@
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@@ -2405,7 +2429,7 @@
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@@ -2452,7 +2476,7 @@
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@@ -2511,7 +2535,7 @@
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@@ -2534,7 +2558,7 @@
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{
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@@ -2558,7 +2582,7 @@
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{
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@@ -2597,7 +2621,7 @@
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{
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"execution_count": 112,
"metadata": {
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@@ -2606,7 +2630,7 @@
"outputs": [],
"source": [
"import tensorflow as tf\n",
"tf.reset_default_graph()\n",
"reset_graph()\n",
"\n",
"n_steps = 50\n",
"n_neurons = 200\n",
@@ -2642,7 +2666,7 @@
},
{
"cell_type": "code",
"execution_count": 112,
"execution_count": 113,
"metadata": {
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"deletable": true,

1323
15_autoencoders.ipynb
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