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n1_image_to_image.py
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import tensorflow as tf
import numpy as np
from collections import namedtuple
import datetime
from n0_network import base_network as b_network
import ocr_utils
class network( b_network):
''' definition of the network
'''
def __init__(self, truthed_features, dtype=np.float32):
self._sess = tf.InteractiveSession()
lst = []
extra_features_width = 0 # width of extra features
"""# ==============================================================================
Placeholders
Compute the size of various layers
Create a tensorflow Placeholder for each feature of data returned from the
dataset
"""# ==============================================================================
for i,nm in enumerate(truthed_features.feature_names):
# features[0], is always the target. For instance it may be m_label_one_hot
# the second features[1] is the 'image' that is passed to the convolution layers
# Any additional features bypass the convolution layers and go directly
# into the fully connected layer.
# The width of the extra features is calculated in order to allocate
# the correct widths of weights, # and inputs
# names are assigned to make the look pretty on the tensorboard graph.
if i == 0:
nm = 'y_'+nm
else:
nm = 'x_'+nm
if i>1:
extra_features_width += truthed_features.feature_width[i]
lst.append(tf.placeholder(dtype, shape=[None, truthed_features.feature_width[i]], name=nm))
# ph is a named tuple with key names like 'image', 'm_label', and values that
# are tensors. The display name on the Chrome graph are 'y_m_label', 'x_image,
# x_upper_case etc.
Place_Holders = namedtuple('Place_Holders', truthed_features.feature_names)
self._ph = Place_Holders(*lst) # unpack placeholders into named Tuple
self._keep_prob = tf.placeholder(dtype,name='keep_prob')
self._nRows = truthed_features.num_rows #image height
self._nCols = truthed_features.num_columns #image width
nFc0 = 2048 # size of fully connected layer
nFc1 = 100 # size of fully connected layer
nFc2 = self._nRows*self._nCols # size of fully connected layer
nConv1 = 32 # size of first convolution layer
nConv2 = 64 # size of second convolution layer
nTarget = truthed_features.feature_width[0] # the number of one_hot features in the target, 'm_label'
n_h_pool2_outputs = int(self._nRows/4) * int(self._nCols/4) * nConv2 # second pooling layer
n_h_pool2_outputsx = n_h_pool2_outputs + extra_features_width # fully connected
"""# ==============================================================================
Build a Multilayer Convolutional Network
Weight Initialization
"""# ==============================================================================
def weight_variable(shape, dtype):
initial = tf.truncated_normal(shape, stddev=0.1,dtype=dtype)
return tf.Variable(initial)
def bias_variable(shape, dtype):
initial = tf.constant(0, shape=shape, dtype=dtype)
return tf.Variable(initial)
"""# ==============================================================================
Convolution and Pooling
keep our code cleaner, let's also abstract those operations into functions.
"""# ==============================================================================
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
"""# ==============================================================================
First Convolutional Layer
"""# ==============================================================================
with tf.name_scope("w_conv1") as scope:
W_conv1 = weight_variable([5, 5, 1, nConv1],dtype)
b_conv1 = bias_variable([nConv1],dtype)
with tf.name_scope("reshape_x_image") as scope:
self._x_image = tf.reshape(self._ph.image, [-1,self._nCols,self._nRows,1])
image_summ = tf.summary.image("x_image", self._x_image)
"""# ==============================================================================
We then convolve x_image with the weight tensor, add the bias, apply the ReLU function,
and finally max pool.
"""# ==============================================================================
with tf.name_scope("convolve_1") as scope:
h_conv1 = tf.nn.relu(conv2d(self._x_image, W_conv1) + b_conv1)
with tf.name_scope("pool_1") as scope:
h_pool1 = max_pool_2x2(h_conv1)
"""# ==============================================================================
Second Convolutional Layer
In order to build a deep network, we stack several layers of this type. The second
layer will have 64 features for each 5x5 patch.
"""# ==============================================================================
with tf.name_scope("convolve_2") as scope:
W_conv2 = weight_variable([5, 5, nConv1, nConv2],dtype)
b_conv2 = bias_variable([64],dtype)
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
with tf.name_scope("pool_2") as scope:
h_pool2 = max_pool_2x2(h_conv2)
"""# ==============================================================================
Densely Connected Layer
Now that the image size has been reduced to 7x7, we add a fully-connected layer
with neurons to allow processing on the entire image. We reshape the tensor
from the pooling layer into a batch of vectors, multiply by a weight matrix, add
a bias, and apply a ReLU.
"""# ==============================================================================
with tf.name_scope("W_fc1_b") as scope:
W_fc0 = weight_variable([n_h_pool2_outputsx, nFc0],dtype)
b_fc0 = bias_variable([nFc0],dtype)
h_pool2_flat = tf.reshape(h_pool2, [-1, n_h_pool2_outputs])
# append the features, the 2nd on, that go directly to the fully connected layer
for i in range(2,truthed_features.num_features ):
h_pool2_flat = tf.concat(1, [h_pool2_flat, self._ph[i]])
h_fc0 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc0) + b_fc0)
"""# ==============================================================================
Densely Connected Layer 1
We add a fully-connected layer
with neurons to allow processing on the entire image. We reshape the tensor
from the pooling layer into a batch of vectors, multiply by a weight matrix, add
a bias, and apply a ReLU.
"""# ==============================================================================
with tf.name_scope("W_fc1_b") as scope:
W_fc1 = weight_variable([nFc0, nFc1],dtype)
b_fc1 = bias_variable([nFc1],dtype)
h_fc1 = tf.nn.relu(tf.matmul(h_fc0, W_fc1) + b_fc1)
"""# ==============================================================================
Densely Connected Layer 2
We add a fully-connected layer
with neurons to allow processing on the entire image. We reshape the tensor
from the pooling layer into a batch of vectors, multiply by a weight matrix, add
a bias, and apply a ReLU.
"""# ==============================================================================
with tf.name_scope("W_fc2_b") as scope:
W_fc2 = weight_variable([nFc1, nFc2],dtype)
b_fc2 = bias_variable([nFc2],dtype)
h_fc2 = tf.nn.relu(tf.matmul(h_fc1, W_fc2) + b_fc2)
"""# ==============================================================================
Dropout
"""# ==============================================================================
with tf.name_scope("drop") as scope:
h_fc2_drop = tf.nn.dropout(h_fc2, self._keep_prob)
"""# ==============================================================================
Readout Layer
"""# ==============================================================================
with tf.name_scope("softmax") as scope:
W_fc3 = weight_variable([nFc2, nTarget],dtype)
b_fc3 = bias_variable([nTarget],dtype)
y_conv=tf.nn.softmax(tf.matmul(h_fc2_drop, W_fc3) + b_fc3)
with tf.name_scope("xent") as scope:
# 1e-8 added to eliminate the crash of training when taking log of 0
self._cross_entropy = -tf.reduce_sum(self._ph[0]*tf.log(y_conv+ 1e-8 ))
#cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
# logits, labels, name='xentropy')
ce_summ = tf.summary.scalar("cross entropy", self._cross_entropy)
with tf.name_scope("reshape_x_image2") as scope:
self._x_image2 = tf.reshape(self._ph[0], [-1,int(self._nCols/2),int(self._nRows/2),1])
image_summ2 = tf.summary.image("x_image2", self._x_image2)
with tf.name_scope("train") as scope:
self._train_step = tf.train.AdamOptimizer(1e-4).minimize(self._cross_entropy)
#self._train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
with tf.name_scope("test") as scope:
self._correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(self._ph[0],1))
self._prediction = tf.argmax(y_conv,1)
self._accuracy = tf.reduce_mean(tf.cast(self._correct_prediction, dtype))
accuracy_summary = tf.summary.scalar("accuracy", self._accuracy)
"""# ==============================================================================
Start TensorFlow Session
"""# ==============================================================================
self._sess.run(tf.initialize_all_variables())
self._merged = tf.summary.merge_all()
tm = ""
tp = datetime.datetime.now().timetuple()
for i in range(4):
tm += str(tp[i])+'-'
tm += str(tp[4])
self._writer = tf.summary.FileWriter("/tmp/ds_logs/"+ tm, self._sess.graph)
def computeSize(s,tens):
sumC = 1
tShape = tens.get_shape()
nDims = len(tShape)
for i in range(nDims):
sumC *= tShape[i].value
print ('\t{}\t{}'.format(s,sumC),flush=True)
return sumC
print ('network size:',flush=True)
total = computeSize("W_fc0",W_fc0)+ \
computeSize ("b_fc0",b_fc0) + \
computeSize ("W_conv1",W_conv1) + \
computeSize ("b_conv1",b_conv1) + \
computeSize ("W_conv2",W_conv2) + \
computeSize ("b_conv2",b_conv2) + \
computeSize ("W_fc0",W_fc0) + \
computeSize ("b_fc0",b_fc0) + \
computeSize ("W_fc1",W_fc1) + \
computeSize ("b_fc1",b_fc1) + \
computeSize ("W_fc2",W_fc2) + \
computeSize ("b_fc2",b_fc2)
print('\ttotal\t{}'.format(total),flush=True)
def reset_graph(self):
tf.reset_default_graph() # only necessary when iterating through fonts
self._sess.close()
def test2(self, truthed_data, title = ''):
# assign feature data to each placeholder
output_images = np.empty((0,int(self._nRows/2),int(self._nCols/2)))
input_images = np.empty((0,int(self._nRows),int(self._nCols)))
test_accuracy=0
m=0
for i in range(int(len(truthed_data.features[0])/100)):
batch = truthed_data.next_batch(100)
# assign feature data to each placeholder
# the batch list is returned in the same order as the features requested
feed = {self._keep_prob: 1.0}
for j in range(truthed_data.num_features):
feed[self._ph[j]] = batch[j]
result = self._sess.run([self._accuracy, self._x_image, self._correct_prediction, self._x_image2], feed_dict=feed)
test_accuracy += result[0]
input_images = np.append(input_images, result[1][:,:,:,0],axis=0)
output_images = np.append(output_images, result[3][:,:,:,0],axis=0)
m += 1
try:
print ("test accuracy {} for : {}".format(test_accuracy/m, title),flush=True)
ocr_utils.montage(input_images,title='TensorFlow {} Input Images'.format(title))
ocr_utils.montage(output_images,title='TensorFlow {} Output Images'.format(title))
except:
if m==0:
print ("test accuracy 1",flush=True)
else:
print ("test accuracy {}".format(test_accuracy/m),flush=True)
ocr_utils.montage(output_images,title='TensorFlow Output Images')
ocr_utils.montage(input_images,title='TensorFlow Input Images')
def fit_entropy(self, truthed_data, nEpochs=5000):
perfect_count=10
for i in range(nEpochs):
batch = truthed_data.next_batch(100)
# assign feature data to each placeholder
# the batch list is returned in the same order as the features requested
feed = {self._keep_prob: 0.5}
for j in range(truthed_data.num_features):
feed[self._ph[j]] = batch[j]
if i%100 == 0:
feed[self._keep_prob] = 1.0
result = self._sess.run([self._merged, self._cross_entropy ], feed_dict=feed)
summary_str = result[0]
self._writer.add_summary(summary_str, i)
train_entropy = result[1]
if train_entropy >= (2000 ):
perfect_count=10;
else:
perfect_count -= 1
if perfect_count==0:
break;
print ("step %d, training entropy %g"%(i, train_entropy),flush=True)
self._sess.run(self._train_step,feed_dict=feed)