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4_covnolutions.py
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import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
from six.moves import range
pickle_file = 'notMNIST.pickle'
'''
save = {
'train_dataset': train_dataset ,
'train_labels' : train_labels ,
'valid_dataset' : valid_dataset ,
'valid_labels' : valid_labels ,
'test_dataset' : test_datasets ,
'test_labels' : test_labels
}
'''
with open(pickle_file , 'rb') as f:
save = pickle.load(f)
train_dataset = save['train_dataset']
train_labels = save['train_labels']
valid_dataset = save['valid_dataset']
valid_labels = save['valid_labels']
test_dataset = save['test_dataset']
test_labels = save['test_labels']
del save
print 'Training set' , train_dataset.shape , train_labels.shape
print 'Validation set' , valid_dataset.shape , valid_labels.shape
print 'Test set' , test_dataset.shape , test_labels.shape
# Reformat into a Tensorflow-friendly shape
image_size = 28
num_labels = 10
num_channels = 1 # grayscale
def reformat(dataset , labels):
dataset = dataset.reshape((-1 , image_size , image_size , num_channels)).astype(np.float32)
labels = (np.arange(num_labels) == labels[: , None]).astype(np.float32)
return dataset , labels
train_dataset , train_labels = reformat(train_dataset , train_labels)
valid_dataset , valid_labels = reformat(valid_dataset , valid_labels)
test_dataset , test_labels = reformat(test_dataset , test_labels)
print 'Training set' , train_dataset.shape , train_labels.shape
print 'Validation set' , valid_dataset.shape , valid_labels.shape
print 'Test set' , test_dataset.shape , test_labels.shape
def accuracy(predictions , labels):
return (100.0 * np.sum(np.argmax(predictions , 1) == np.argmax(labels , 1)) / predictions.shape[0])
# build a small network
batch_size = 16
patch_size = 5
depth = 16
num_hidden = 64
graph = tf.Graph()
with graph.as_default():
# Input data
tf_train_dataset = tf.placeholder(tf.float32 , shape = (batch_size , image_size , image_size , num_channels))
tf_train_labels = tf.placeholder(tf.float32 , shape = (batch_size , num_labels))
tf_valid_dataset = tf.constant(valid_dataset)
tf_test_dataset = tf.constant(test_dataset)
#Variables
layer1_weights = tf.Variable(tf.truncated_normal([patch_size , patch_size , num_channels, depth] , stddev = 0.1))
layer1_biases = tf.Variable(tf.zeros([depth]))
layer2_weights = tf.Variable(tf.truncated_normal([patch_size , patch_size , depth , depth] , stddev = 0.1))
layer2_biases = tf.Variable(tf.constant(1.0 , shape = [depth]))
layer3_weights = tf.Variable(tf.truncated_normal([image_size // 4 * image_size // 4 * depth , num_hidden] , stddev = 0.1))
layer3_biases = tf.Variable(tf.constant(1.0 , shape = [num_hidden]))
layer4_weights = tf.Variable(tf.truncated_normal([num_hidden , num_labels] , stddev = 0.1))
layer4_biases = tf.Variable(tf.constant(1.0 , shape = [num_labels]))
#Model
def model(data):
#stride[1 , x_move , y_move , 1]
# zhu
conv = tf.nn.conv2d(data , layer1_weights , strides = [1 , 2 , 2 , 1] , padding = 'SAME')
hidden = tf.nn.relu(conv + layer1_biases)
#print 'no pool: ' , hidden.get_shape().as_list()
hidden = tf.nn.max_pool(hidden , ksize = [1 , 2, 2 , 1] , strides = [1 , 2 , 2 , 1] ,padding = 'SAME')
print '1st pool: ' , hidden.get_shape().as_list()
conv = tf.nn.conv2d(hidden , layer2_weights , strides = [1 ,1 , 1 , 1] , padding = 'SAME')
hidden =tf.nn.relu(conv + layer2_biases)
#hidden = tf.nn.max_pool(hidden , ksize =[1 , 2 , 2 , 1] , strides = [1 , 2 , 2 , 1] , padding = 'SAME')
#print '1st pool: ' , hidden.get_shape().as_list()
shape = hidden.get_shape().as_list()
print shape
reshape =tf.reshape(hidden , [shape[0] , shape[1] * shape[2] * shape[3]])
hidden = tf.nn.relu(tf.matmul(reshape , layer3_weights) + layer3_biases)
return tf.matmul(hidden , layer4_weights) + layer4_biases
# Training computation
logits = model(tf_train_dataset)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits , tf_train_labels))
#Optimizer
optimizer = tf.train.GradientDescentOptimizer(0.05).minimize(loss)
#Predictions for train , valid , test data
train_pre = tf.nn.softmax(logits)
valid_pre = tf.nn.softmax(model(tf_valid_dataset))
test_pre = tf.nn.softmax(model(tf_test_dataset))
num_steps = 1001
with tf.Session(graph = graph) as session:
tf.initialize_all_variables().run()
print 'Initialized'
for step in range(num_steps):
offset = (step * batch_size) % (train_labels.shape[0] - batch_size)
batch_data = train_dataset[offset : (batch_size + offset) , : , : , :]
batch_labels = train_labels[offset : (batch_size + offset) , :]
feed_dict = {tf_train_dataset : batch_data , tf_train_labels : batch_labels}
_ , l , predictions = session.run([optimizer , loss , train_pre] , feed_dict = feed_dict)
if step % 50 == 0 :
print 'Minibatch loss at step %d: %f' % (step , l)
print 'Minibatch accuracy : %.1f%%' % accuracy(predictions , batch_labels)
print 'Validation accuracy : %.1f%%' % accuracy(valid_pre.eval() , valid_labels)
print 'Test accuracy : %.1f%%' % accuracy(test_pre.eval() , test_labels)