Wing IDE编译TesorFlow中Mnist convolutional 实例,tesorflowmnist

  1 #
  2 #     http://www.cnblogs.com/mydebug/
  3 #
  4 from __future__ import absolute_import
  5 from __future__ import division
  6 from __future__ import print_function
  7 
  8 import gzip
  9 import os
 10 import sys
 11 sys.path.append("这里是numpy的路径")//提示:如果没有这句import numpy会报错
 12 import tensorflow.python.platform
 13 
 14 import numpy
 15 from six.moves import urllib
 16 from six.moves import xrange  # pylint: disable=redefined-builtin
 17 import tensorflow as tf
 18 
 19 SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
 20 WORK_DIRECTORY = '/TensorFlow/data'
 21 IMAGE_SIZE = 28
 22 NUM_CHANNELS = 1
 23 PIXEL_DEPTH = 255
 24 NUM_LABELS = 10
 25 VALIDATION_SIZE = 5000  # Size of the validation set.
 26 SEED = 66478  # Set to None for random seed.
 27 BATCH_SIZE = 64
 28 NUM_EPOCHS = 10
 29 
 30 
 31 tf.app.flags.DEFINE_boolean("self_test", False, "True if running a self test.")
 32 FLAGS = tf.app.flags.FLAGS
 33 
 34 
 35 def maybe_download(filename):
 36   """Download the data from Yann's website, unless it's already here."""
 37   if not os.path.exists(WORK_DIRECTORY):
 38     os.mkdir(WORK_DIRECTORY)
 39   filepath = os.path.join(WORK_DIRECTORY, filename)
 40   if not os.path.exists(filepath):
 41     filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)
 42     statinfo = os.stat(filepath)
 43     print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')
 44   return filepath
 45 
 46 
 47 def extract_data(filename, num_images):
 48   """Extract the images into a 4D tensor [image index, y, x, channels].
 49 
 50   Values are rescaled from [0, 255] down to [-0.5, 0.5].
 51   """
 52   print('Extracting', filename)
 53   with gzip.open(filename) as bytestream:
 54     bytestream.read(16)
 55     buf = bytestream.read(IMAGE_SIZE * IMAGE_SIZE * num_images)
 56     data = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.float32)
 57     data = (data - (PIXEL_DEPTH / 2.0)) / PIXEL_DEPTH
 58     data = data.reshape(num_images, IMAGE_SIZE, IMAGE_SIZE, 1)
 59     return data
 60 
 61 
 62 def extract_labels(filename, num_images):
 63   """Extract the labels into a 1-hot matrix [image index, label index]."""
 64   print('Extracting', filename)
 65   with gzip.open(filename) as bytestream:
 66     bytestream.read(8)
 67     buf = bytestream.read(1 * num_images)
 68     labels = numpy.frombuffer(buf, dtype=numpy.uint8)
 69   # Convert to dense 1-hot representation.
 70   return (numpy.arange(NUM_LABELS) == labels[:, None]).astype(numpy.float32)
 71 
 72 
 73 def fake_data(num_images):
 74   """Generate a fake dataset that matches the dimensions of MNIST."""
 75   data = numpy.ndarray(
 76       shape=(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS),
 77       dtype=numpy.float32)
 78   labels = numpy.zeros(shape=(num_images, NUM_LABELS), dtype=numpy.float32)
 79   for image in xrange(num_images):
 80     label = image % 2
 81     data[image, :, :, 0] = label - 0.5
 82     labels[image, label] = 1.0
 83   return data, labels
 84 
 85 
 86 def error_rate(predictions, labels):
 87   """Return the error rate based on dense predictions and 1-hot labels."""
 88   return 100.0 - (
 89       100.0 *
 90       numpy.sum(numpy.argmax(predictions, 1) == numpy.argmax(labels, 1)) /
 91       predictions.shape[0])
 92 
 93 
 94 def main(argv=None):  # pylint: disable=unused-argument
 95   if FLAGS.self_test:
 96     print('Running self-test.')
 97     train_data, train_labels = fake_data(256)
 98     validation_data, validation_labels = fake_data(16)
 99     test_data, test_labels = fake_data(256)
100     num_epochs = 1
101   else:
102     # Get the data.
103     train_data_filename = maybe_download('train-images-idx3-ubyte.gz')
104     train_labels_filename = maybe_download('train-labels-idx1-ubyte.gz')
105     test_data_filename = maybe_download('t10k-images-idx3-ubyte.gz')
106     test_labels_filename = maybe_download('t10k-labels-idx1-ubyte.gz')
107 
108     # Extract it into numpy arrays.
109     train_data = extract_data(train_data_filename, 60000)
110     train_labels = extract_labels(train_labels_filename, 60000)
111     test_data = extract_data(test_data_filename, 10000)
112     test_labels = extract_labels(test_labels_filename, 10000)
113 
114     # Generate a validation set.
115     validation_data = train_data[:VALIDATION_SIZE, :, :, :]
116     validation_labels = train_labels[:VALIDATION_SIZE]
117     train_data = train_data[VALIDATION_SIZE:, :, :, :]
118     train_labels = train_labels[VALIDATION_SIZE:]
119     num_epochs = NUM_EPOCHS
120   train_size = train_labels.shape[0]
121 
122   # This is where training samples and labels are fed to the graph.
123   # These placeholder nodes will be fed a batch of training data at each
124   # training step using the {feed_dict} argument to the Run() call below.
125   train_data_node = tf.placeholder(
126       tf.float32,
127       shape=(BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))
128   train_labels_node = tf.placeholder(tf.float32,
129                                      shape=(BATCH_SIZE, NUM_LABELS))
130   # For the validation and test data, we'll just hold the entire dataset in
131   # one constant node.
132   validation_data_node = tf.constant(validation_data)
133   test_data_node = tf.constant(test_data)
134 
135   # The variables below hold all the trainable weights. They are passed an
136   # initial value which will be assigned when when we call:
137   # {tf.initialize_all_variables().run()}
138   conv1_weights = tf.Variable(
139       tf.truncated_normal([5, 5, NUM_CHANNELS, 32],  # 5x5 filter, depth 32.
140                           stddev=0.1,
141                           seed=SEED))
142   conv1_biases = tf.Variable(tf.zeros([32]))
143   conv2_weights = tf.Variable(
144       tf.truncated_normal([5, 5, 32, 64],
145                           stddev=0.1,
146                           seed=SEED))
147   conv2_biases = tf.Variable(tf.constant(0.1, shape=[64]))
148   fc1_weights = tf.Variable(  # fully connected, depth 512.
149       tf.truncated_normal(
150           [IMAGE_SIZE // 4 * IMAGE_SIZE // 4 * 64, 512],
151           stddev=0.1,
152           seed=SEED))
153   fc1_biases = tf.Variable(tf.constant(0.1, shape=[512]))
154   fc2_weights = tf.Variable(
155       tf.truncated_normal([512, NUM_LABELS],
156                           stddev=0.1,
157                           seed=SEED))
158   fc2_biases = tf.Variable(tf.constant(0.1, shape=[NUM_LABELS]))
159 
160   # We will replicate the model structure for the training subgraph, as well
161   # as the evaluation subgraphs, while sharing the trainable parameters.
162   def model(data, train=False):
163     """The Model definition."""
164     # 2D convolution, with 'SAME' padding (i.e. the output feature map has
165     # the same size as the input). Note that {strides} is a 4D array whose
166     # shape matches the data layout: [image index, y, x, depth].
167     conv = tf.nn.conv2d(data,
168                         conv1_weights,
169                         strides=[1, 1, 1, 1],
170                         padding='SAME')
171     # Bias and rectified linear non-linearity.
172     relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases))
173     # Max pooling. The kernel size spec {ksize} also follows the layout of
174     # the data. Here we have a pooling window of 2, and a stride of 2.
175     pool = tf.nn.max_pool(relu,
176                           ksize=[1, 2, 2, 1],
177                           strides=[1, 2, 2, 1],
178                           padding='SAME')
179     conv = tf.nn.conv2d(pool,
180                         conv2_weights,
181                         strides=[1, 1, 1, 1],
182                         padding='SAME')
183     relu = tf.nn.relu(tf.nn.bias_add(conv, conv2_biases))
184     pool = tf.nn.max_pool(relu,
185                           ksize=[1, 2, 2, 1],
186                           strides=[1, 2, 2, 1],
187                           padding='SAME')
188     # Reshape the feature map cuboid into a 2D matrix to feed it to the
189     # fully connected layers.
190     pool_shape = pool.get_shape().as_list()
191     reshape = tf.reshape(
192         pool,
193         [pool_shape[0], pool_shape[1] * pool_shape[2] * pool_shape[3]])
194     # Fully connected layer. Note that the '+' operation automatically
195     # broadcasts the biases.
196     hidden = tf.nn.relu(tf.matmul(reshape, fc1_weights) + fc1_biases)
197     # Add a 50% dropout during training only. Dropout also scales
198     # activations such that no rescaling is needed at evaluation time.
199     if train:
200       hidden = tf.nn.dropout(hidden, 0.5, seed=SEED)
201     return tf.matmul(hidden, fc2_weights) + fc2_biases
202 
203   # Training computation: logits + cross-entropy loss.
204   logits = model(train_data_node, True)
205   loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
206       logits, train_labels_node))
207 
208   # L2 regularization for the fully connected parameters.
209   regularizers = (tf.nn.l2_loss(fc1_weights) + tf.nn.l2_loss(fc1_biases) +
210                   tf.nn.l2_loss(fc2_weights) + tf.nn.l2_loss(fc2_biases))
211   # Add the regularization term to the loss.
212   loss += 5e-4 * regularizers
213 
214   # Optimizer: set up a variable that's incremented once per batch and
215   # controls the learning rate decay.
216   batch = tf.Variable(0)
217   # Decay once per epoch, using an exponential schedule starting at 0.01.
218   learning_rate = tf.train.exponential_decay(
219       0.01,                # Base learning rate.
220       batch * BATCH_SIZE,  # Current index into the dataset.
221       train_size,          # Decay step.
222       0.95,                # Decay rate.
223       staircase=True)
224   # Use simple momentum for the optimization.
225   optimizer = tf.train.MomentumOptimizer(learning_rate,
226                                          0.9).minimize(loss,
227                                                        global_step=batch)
228 
229   # Predictions for the minibatch, validation set and test set.
230   train_prediction = tf.nn.softmax(logits)
231   # We'll compute them only once in a while by calling their {eval()} method.
232   validation_prediction = tf.nn.softmax(model(validation_data_node))
233   test_prediction = tf.nn.softmax(model(test_data_node))
234 
235   # Create a local session to run this computation.
236   with tf.Session() as s:
237     # Run all the initializers to prepare the trainable parameters.
238     tf.initialize_all_variables().run()
239     print('Initialized!')
240     # Loop through training steps.
241     for step in xrange(num_epochs * train_size // BATCH_SIZE):
242       # Compute the offset of the current minibatch in the data.
243       # Note that we could use better randomization across epochs.
244       offset = (step * BATCH_SIZE) % (train_size - BATCH_SIZE)
245       batch_data = train_data[offset:(offset + BATCH_SIZE), :, :, :]
246       batch_labels = train_labels[offset:(offset + BATCH_SIZE)]
247       # This dictionary maps the batch data (as a numpy array) to the
248       # node in the graph is should be fed to.
249       feed_dict = {train_data_node: batch_data,
250                    train_labels_node: batch_labels}
251       # Run the graph and fetch some of the nodes.
252       _, l, lr, predictions = s.run(
253           [optimizer, loss, learning_rate, train_prediction],
254           feed_dict=feed_dict)
255       if step % 100 == 0:
256         print('Epoch %.2f' % (float(step) * BATCH_SIZE / train_size))
257         print('Minibatch loss: %.3f, learning rate: %.6f' % (l, lr))
258         print('Minibatch error: %.1f%%' % error_rate(predictions, batch_labels))
259         print('Validation error: %.1f%%' %
260               error_rate(validation_prediction.eval(), validation_labels))
261         sys.stdout.flush()
262     # Finally print the result!
263     test_error = error_rate(test_prediction.eval(), test_labels)
264     print('Test error: %.1f%%' % test_error)
265     if FLAGS.self_test:
266       print('test_error', test_error)
267       assert test_error == 0.0, 'expected 0.0 test_error, got %.2f' % (
268           test_error,)
269 
270 
271 if __name__ == '__main__':
272   tf.app.run()

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注:注意源代码中的提示部分。

IDE编译TesorFlow中Mnist convolutional
实例,tesorflowmnist 1 # 2 # 3 # 4
from __future__ import absolute_import 5 from __future__
import…

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