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Notebook for CNN on MNIST data.
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01-MNIST-with-CNN-Copy1.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MNIST with CNN"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import tensorflow as tf"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from tensorflow.examples.tutorials.mnist import input_data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
"Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
"Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
"Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
]
}
],
"source": [
"mnist = input_data.read_data_sets(\"MNIST_data/\",one_hot=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensorflow.contrib.learn.python.learn.datasets.base.Datasets"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(mnist)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" ...,\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mnist.train.images"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(55000, 784)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mnist.train.images.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Helper Functions"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(10000, 784)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mnist.test.images.shape"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10000"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mnist.test.num_examples"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
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"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"mnist.train.images[1]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
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" 0. , 0.12156864, 0.36078432, 0.6784314 , 0.9921569 ,\n",
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" 0.7960785 , 0.91372555, 0.98823535, 0.9921569 , 0.98823535,\n",
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" [0. , 0. , 0. , 0. , 0. ,\n",
" 0.08235294, 0.7960785 , 1. , 0.9921569 , 0.9960785 ,\n",
" 0.9921569 , 0.9960785 , 0.9921569 , 0.9568628 , 0.7960785 ,\n",
" 0.32156864, 0. , 0. , 0. , 0. ,\n",
" 0. , 0. , 0. , 0. , 0. ,\n",
" 0. , 0. , 0. ],\n",
" [0. , 0. , 0. , 0. , 0. ,\n",
" 0. , 0.07843138, 0.5921569 , 0.5921569 , 0.9921569 ,\n",
" 0.67058825, 0.5921569 , 0.5921569 , 0.15686275, 0. ,\n",
" 0. , 0. , 0. , 0. , 0. ,\n",
" 0. , 0. , 0. , 0. , 0. ,\n",
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" [0. , 0. , 0. , 0. , 0. ,\n",
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" 0. , 0. , 0. , 0. , 0. ,\n",
" 0. , 0. , 0. ]], dtype=float32)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mnist.train.images[1].reshape(28,28)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"single_image = mnist.train.images[1].reshape(28,28)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x13356a0b8>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x1314719b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(single_image, cmap='gist_gray')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"single_image.min()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"single_image.max()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"second_image = mnist.train.images[2].reshape(28,28)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x13359b0f0>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x1336e8400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(second_image)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# PLACEHOLDERS\n",
"x = tf.placeholder(tf.float32, shape=[None,784])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# VARIABLES\n",
"W = tf.Variable(tf.zeros([784,10]))\n",
"b = tf.Variable(tf.zeros([10]))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create Graph Opertationsf\n",
"y = tf.matmul(x,W) + b"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Loss Function\n",
"y_true = tf.placeholder(tf.float32, shape=[None, 10])"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = y_true, logits = y))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Optimizer\n",
"optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.5)\n",
"train = optimizer.minimize(cross_entropy)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create session\n",
"init = tf.global_variables_initializer()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"ename": "InvalidArgumentError",
"evalue": "You must feed a value for placeholder tensor 'Placeholder_1' with dtype float and shape [?,10]\n\t [[Node: Placeholder_1 = Placeholder[dtype=DT_FLOAT, shape=[?,10], _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]\n\nCaused by op 'Placeholder_1', defined at:\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/runpy.py\", line 193, in _run_module_as_main\n \"__main__\", mod_spec)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/runpy.py\", line 85, in _run_code\n exec(code, run_globals)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel_launcher.py\", line 16, in <module>\n app.launch_new_instance()\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n app.start()\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel/kernelapp.py\", line 477, in start\n ioloop.IOLoop.instance().start()\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/zmq/eventloop/ioloop.py\", line 177, in start\n super(ZMQIOLoop, self).start()\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tornado/ioloop.py\", line 888, in start\n handler_func(fd_obj, events)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n return fn(*args, **kwargs)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\n self._handle_recv()\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\n self._run_callback(callback, msg)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\n callback(*args, **kwargs)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n return fn(*args, **kwargs)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n return self.dispatch_shell(stream, msg)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 235, in dispatch_shell\n handler(stream, idents, msg)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n user_expressions, allow_stdin)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel/ipkernel.py\", line 196, in do_execute\n res = shell.run_cell(code, store_history=store_history, silent=silent)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel/zmqshell.py\", line 533, in run_cell\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2698, in run_cell\n interactivity=interactivity, compiler=compiler, result=result)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2802, in run_ast_nodes\n if self.run_code(code, result):\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2862, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"<ipython-input-21-12e74ec33d57>\", line 2, in <module>\n y_true = tf.placeholder(tf.float32, shape=[None, 10])\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py\", line 1548, in placeholder\n return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/ops/gen_array_ops.py\", line 2094, in _placeholder\n name=name)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py\", line 767, in apply_op\n op_def=op_def)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\", line 2630, in create_op\n original_op=self._default_original_op, op_def=op_def)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\", line 1204, in __init__\n self._traceback = self._graph._extract_stack() # pylint: disable=protected-access\n\nInvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder_1' with dtype float and shape [?,10]\n\t [[Node: Placeholder_1 = Placeholder[dtype=DT_FLOAT, shape=[?,10], _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]\n",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m 1326\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1327\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1328\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m 1305\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1306\u001b[0;31m status, run_metadata)\n\u001b[0m\u001b[1;32m 1307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/envs/tfdeeplearning/lib/python3.5/contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type, value, traceback)\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 67\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py\u001b[0m in \u001b[0;36mraise_exception_on_not_ok_status\u001b[0;34m()\u001b[0m\n\u001b[1;32m 465\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpywrap_tensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 466\u001b[0;31m pywrap_tensorflow.TF_GetCode(status))\n\u001b[0m\u001b[1;32m 467\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mInvalidArgumentError\u001b[0m: You must feed a value for placeholder tensor 'Placeholder_1' with dtype float and shape [?,10]\n\t [[Node: Placeholder_1 = Placeholder[dtype=DT_FLOAT, shape=[?,10], _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-25-1df33799cd1a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mbatch_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmnist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mbatch_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mbatch_y\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;31m# Evaluate the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 893\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 894\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 895\u001b[0;31m run_metadata_ptr)\n\u001b[0m\u001b[1;32m 896\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 897\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 1122\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1123\u001b[0m results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1124\u001b[0;31m feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m 1125\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1126\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 1319\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1320\u001b[0m return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1321\u001b[0;31m options, run_metadata)\n\u001b[0m\u001b[1;32m 1322\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1323\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m 1338\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1339\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1340\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1341\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1342\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mInvalidArgumentError\u001b[0m: You must feed a value for placeholder tensor 'Placeholder_1' with dtype float and shape [?,10]\n\t [[Node: Placeholder_1 = Placeholder[dtype=DT_FLOAT, shape=[?,10], _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]\n\nCaused by op 'Placeholder_1', defined at:\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/runpy.py\", line 193, in _run_module_as_main\n \"__main__\", mod_spec)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/runpy.py\", line 85, in _run_code\n exec(code, run_globals)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel_launcher.py\", line 16, in <module>\n app.launch_new_instance()\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n app.start()\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel/kernelapp.py\", line 477, in start\n ioloop.IOLoop.instance().start()\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/zmq/eventloop/ioloop.py\", line 177, in start\n super(ZMQIOLoop, self).start()\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tornado/ioloop.py\", line 888, in start\n handler_func(fd_obj, events)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n return fn(*args, **kwargs)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\n self._handle_recv()\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\n self._run_callback(callback, msg)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\n callback(*args, **kwargs)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n return fn(*args, **kwargs)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n return self.dispatch_shell(stream, msg)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 235, in dispatch_shell\n handler(stream, idents, msg)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n user_expressions, allow_stdin)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel/ipkernel.py\", line 196, in do_execute\n res = shell.run_cell(code, store_history=store_history, silent=silent)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/ipykernel/zmqshell.py\", line 533, in run_cell\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2698, in run_cell\n interactivity=interactivity, compiler=compiler, result=result)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2802, in run_ast_nodes\n if self.run_code(code, result):\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2862, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"<ipython-input-21-12e74ec33d57>\", line 2, in <module>\n y_true = tf.placeholder(tf.float32, shape=[None, 10])\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py\", line 1548, in placeholder\n return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/ops/gen_array_ops.py\", line 2094, in _placeholder\n name=name)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py\", line 767, in apply_op\n op_def=op_def)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\", line 2630, in create_op\n original_op=self._default_original_op, op_def=op_def)\n File \"/Users/majain/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\", line 1204, in __init__\n self._traceback = self._graph._extract_stack() # pylint: disable=protected-access\n\nInvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder_1' with dtype float and shape [?,10]\n\t [[Node: Placeholder_1 = Placeholder[dtype=DT_FLOAT, shape=[?,10], _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]\n"
]
}
],
"source": [
"with tf.Session() as sess:\n",
" \n",
" sess.run(init)\n",
" \n",
" for step in range(1000):\n",
" \n",
" batch_x, batch_y = mnist.train.next_batch(100)\n",
" \n",
" sess.run(train, feed_dict={x:batch_x, y:batch_y})\n",
" \n",
" # Evaluate the model\n",
" correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_true,1))\n",
" \n",
" # Output will be like [True, False, True.....] --> Cast to [1.0, 0.0, 1.0.....]\n",
" acc = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))\n",
" \n",
" print(sess.run(acc,feed_dict={x:mnist.test.images,y_true:mnist.test.labels}))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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