Wing IDE编译TesorFlow中Mnist convolutional 实例

 #
 #     http://www.cnblogs.com/mydebug/
 #
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

 import gzip
 import os
 import sys
 sys.path.append("这里是numpy的路径")//提示：如果没有这句import numpy会报错
 import tensorflow.python.platform

 import numpy
 from six.moves import urllib
 from six.moves import xrange  # pylint: disable=redefined-builtin
 import tensorflow as tf

 SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
 WORK_DIRECTORY = '/TensorFlow/data'
 IMAGE_SIZE = 28
 NUM_CHANNELS = 1
 PIXEL_DEPTH = 255
 NUM_LABELS = 10
 VALIDATION_SIZE = 5000  # Size of the validation set.
 SEED = 66478  # Set to None for random seed.
 BATCH_SIZE = 64
 NUM_EPOCHS = 10

 tf.app.flags.DEFINE_boolean("self_test", False, "True if running a self test.")
 FLAGS = tf.app.flags.FLAGS

 def maybe_download(filename):
   """Download the data from Yann's website, unless it's already here."""
   if not os.path.exists(WORK_DIRECTORY):
     os.mkdir(WORK_DIRECTORY)
   filepath = os.path.join(WORK_DIRECTORY, filename)
   if not os.path.exists(filepath):
     filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)
     statinfo = os.stat(filepath)
     print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')
   return filepath

 def extract_data(filename, num_images):
   """Extract the images into a 4D tensor [image index, y, x, channels].

   Values are rescaled from [0, 255] down to [-0.5, 0.5].
   """
   print('Extracting', filename)
   with gzip.open(filename) as bytestream:
     bytestream.read(16)
     buf = bytestream.read(IMAGE_SIZE * IMAGE_SIZE * num_images)
     data = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.float32)
     data = (data - (PIXEL_DEPTH / 2.0)) / PIXEL_DEPTH
     data = data.reshape(num_images, IMAGE_SIZE, IMAGE_SIZE, 1)
     return data

 def extract_labels(filename, num_images):
   """Extract the labels into a 1-hot matrix [image index, label index]."""
   print('Extracting', filename)
   with gzip.open(filename) as bytestream:
     bytestream.read(8)
     buf = bytestream.read(1 * num_images)
     labels = numpy.frombuffer(buf, dtype=numpy.uint8)
   # Convert to dense 1-hot representation.
   return (numpy.arange(NUM_LABELS) == labels[:, None]).astype(numpy.float32)

 def fake_data(num_images):
   """Generate a fake dataset that matches the dimensions of MNIST."""
   data = numpy.ndarray(
       shape=(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS),
       dtype=numpy.float32)
   labels = numpy.zeros(shape=(num_images, NUM_LABELS), dtype=numpy.float32)
   for image in xrange(num_images):
     label = image % 2
     data[image, :, :, 0] = label - 0.5
     labels[image, label] = 1.0
   return data, labels

 def error_rate(predictions, labels):
   """Return the error rate based on dense predictions and 1-hot labels."""
   return 100.0 - (
       100.0 *
       numpy.sum(numpy.argmax(predictions, 1) == numpy.argmax(labels, 1)) /
       predictions.shape[0])

 def main(argv=None):  # pylint: disable=unused-argument
   if FLAGS.self_test:
     print('Running self-test.')
     train_data, train_labels = fake_data(256)
     validation_data, validation_labels = fake_data(16)
     test_data, test_labels = fake_data(256)
     num_epochs = 1
   else:
     # Get the data.
     train_data_filename = maybe_download('train-images-idx3-ubyte.gz')
     train_labels_filename = maybe_download('train-labels-idx1-ubyte.gz')
     test_data_filename = maybe_download('t10k-images-idx3-ubyte.gz')
     test_labels_filename = maybe_download('t10k-labels-idx1-ubyte.gz')

     # Extract it into numpy arrays.
     train_data = extract_data(train_data_filename, 60000)
     train_labels = extract_labels(train_labels_filename, 60000)
     test_data = extract_data(test_data_filename, 10000)
     test_labels = extract_labels(test_labels_filename, 10000)

     # Generate a validation set.
     validation_data = train_data[:VALIDATION_SIZE, :, :, :]
     validation_labels = train_labels[:VALIDATION_SIZE]
     train_data = train_data[VALIDATION_SIZE:, :, :, :]
     train_labels = train_labels[VALIDATION_SIZE:]
     num_epochs = NUM_EPOCHS
   train_size = train_labels.shape[0]

   # This is where training samples and labels are fed to the graph.
   # These placeholder nodes will be fed a batch of training data at each
   # training step using the {feed_dict} argument to the Run() call below.
   train_data_node = tf.placeholder(
       tf.float32,
       shape=(BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))
   train_labels_node = tf.placeholder(tf.float32,
                                      shape=(BATCH_SIZE, NUM_LABELS))
   # For the validation and test data, we'll just hold the entire dataset in
   # one constant node.
   validation_data_node = tf.constant(validation_data)
   test_data_node = tf.constant(test_data)

   # The variables below hold all the trainable weights. They are passed an
   # initial value which will be assigned when when we call:
   # {tf.initialize_all_variables().run()}
   conv1_weights = tf.Variable(
       tf.truncated_normal([5, 5, NUM_CHANNELS, 32],  # 5x5 filter, depth 32.
                           stddev=0.1,
                           seed=SEED))
   conv1_biases = tf.Variable(tf.zeros([32]))
   conv2_weights = tf.Variable(
       tf.truncated_normal([5, 5, 32, 64],
                           stddev=0.1,
                           seed=SEED))
   conv2_biases = tf.Variable(tf.constant(0.1, shape=[64]))
   fc1_weights = tf.Variable(  # fully connected, depth 512.
       tf.truncated_normal(
           [IMAGE_SIZE // 4 * IMAGE_SIZE // 4 * 64, 512],
           stddev=0.1,
           seed=SEED))
   fc1_biases = tf.Variable(tf.constant(0.1, shape=[512]))
   fc2_weights = tf.Variable(
       tf.truncated_normal([512, NUM_LABELS],
                           stddev=0.1,
                           seed=SEED))
   fc2_biases = tf.Variable(tf.constant(0.1, shape=[NUM_LABELS]))

   # We will replicate the model structure for the training subgraph, as well
   # as the evaluation subgraphs, while sharing the trainable parameters.
   def model(data, train=False):
     """The Model definition."""
     # 2D convolution, with 'SAME' padding (i.e. the output feature map has
     # the same size as the input). Note that {strides} is a 4D array whose
     # shape matches the data layout: [image index, y, x, depth].
     conv = tf.nn.conv2d(data,
                         conv1_weights,
                         strides=[1, 1, 1, 1],
                         padding='SAME')
     # Bias and rectified linear non-linearity.
     relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases))
     # Max pooling. The kernel size spec {ksize} also follows the layout of
     # the data. Here we have a pooling window of 2, and a stride of 2.
     pool = tf.nn.max_pool(relu,
                           ksize=[1, 2, 2, 1],
                           strides=[1, 2, 2, 1],
                           padding='SAME')
     conv = tf.nn.conv2d(pool,
                         conv2_weights,
                         strides=[1, 1, 1, 1],
                         padding='SAME')
     relu = tf.nn.relu(tf.nn.bias_add(conv, conv2_biases))
     pool = tf.nn.max_pool(relu,
                           ksize=[1, 2, 2, 1],
                           strides=[1, 2, 2, 1],
                           padding='SAME')
     # Reshape the feature map cuboid into a 2D matrix to feed it to the
     # fully connected layers.
     pool_shape = pool.get_shape().as_list()
     reshape = tf.reshape(
         pool,
         [pool_shape[0], pool_shape[1] * pool_shape[2] * pool_shape[3]])
     # Fully connected layer. Note that the '+' operation automatically
     # broadcasts the biases.
     hidden = tf.nn.relu(tf.matmul(reshape, fc1_weights) + fc1_biases)
     # Add a 50% dropout during training only. Dropout also scales
     # activations such that no rescaling is needed at evaluation time.
     if train:
       hidden = tf.nn.dropout(hidden, 0.5, seed=SEED)
     return tf.matmul(hidden, fc2_weights) + fc2_biases

   # Training computation: logits + cross-entropy loss.
   logits = model(train_data_node, True)
   loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
       logits, train_labels_node))

   # L2 regularization for the fully connected parameters.
   regularizers = (tf.nn.l2_loss(fc1_weights) + tf.nn.l2_loss(fc1_biases) +
                   tf.nn.l2_loss(fc2_weights) + tf.nn.l2_loss(fc2_biases))
   # Add the regularization term to the loss.
   loss += 5e-4 * regularizers

   # Optimizer: set up a variable that's incremented once per batch and
   # controls the learning rate decay.
   batch = tf.Variable(0)
   # Decay once per epoch, using an exponential schedule starting at 0.01.
   learning_rate = tf.train.exponential_decay(
       0.01,                # Base learning rate.
       batch * BATCH_SIZE,  # Current index into the dataset.
       train_size,          # Decay step.
       0.95,                # Decay rate.
       staircase=True)
   # Use simple momentum for the optimization.
   optimizer = tf.train.MomentumOptimizer(learning_rate,
                                          0.9).minimize(loss,
                                                        global_step=batch)

   # Predictions for the minibatch, validation set and test set.
   train_prediction = tf.nn.softmax(logits)
   # We'll compute them only once in a while by calling their {eval()} method.
   validation_prediction = tf.nn.softmax(model(validation_data_node))
   test_prediction = tf.nn.softmax(model(test_data_node))

   # Create a local session to run this computation.
   with tf.Session() as s:
     # Run all the initializers to prepare the trainable parameters.
     tf.initialize_all_variables().run()
     print('Initialized!')
     # Loop through training steps.
     for step in xrange(num_epochs * train_size // BATCH_SIZE):
       # Compute the offset of the current minibatch in the data.
       # Note that we could use better randomization across epochs.
       offset = (step * BATCH_SIZE) % (train_size - BATCH_SIZE)
       batch_data = train_data[offset:(offset + BATCH_SIZE), :, :, :]
       batch_labels = train_labels[offset:(offset + BATCH_SIZE)]
       # This dictionary maps the batch data (as a numpy array) to the
       # node in the graph is should be fed to.
       feed_dict = {train_data_node: batch_data,
                    train_labels_node: batch_labels}
       # Run the graph and fetch some of the nodes.
       _, l, lr, predictions = s.run(
           [optimizer, loss, learning_rate, train_prediction],
           feed_dict=feed_dict)
       if step % 100 == 0:
         print('Epoch %.2f' % (float(step) * BATCH_SIZE / train_size))
         print('Minibatch loss: %.3f, learning rate: %.6f' % (l, lr))
         print('Minibatch error: %.1f%%' % error_rate(predictions, batch_labels))
         print('Validation error: %.1f%%' %
               error_rate(validation_prediction.eval(), validation_labels))
         sys.stdout.flush()
     # Finally print the result!
     test_error = error_rate(test_prediction.eval(), test_labels)
     print('Test error: %.1f%%' % test_error)
     if FLAGS.self_test:
       print('test_error', test_error)
       assert test_error == 0.0, 'expected 0.0 test_error, got %.2f' % (
           test_error,)

 if __name__ == '__main__':
   tf.app.run()

在Ming IDE中新建python文件，复制以上代码，点击Debug，运行成功。

注：注意源代码中的提示部分。