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。
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