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n1_residual3x4.py
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'''
takes an image input and trains it to make an image output
funnels down to a 'key' and then goes back up to image
'''
from tensorflow.compat import v1 as tf
import numpy as np
from collections import namedtuple
import datetime
import ocr_utils
from n0_network import base_network as b_network
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', 'self._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
nSections = 10
in_out_width = self._nRows*self._nCols
internal_width = int(in_out_width/4)
w = list(range(nSections*3))
b = list(range(nSections*3))
h = list(range(nSections*3+1))
nFc1 = 2048 # size of fully connected layer
nTarget = truthed_features.feature_width[0] # the number of one_hot features in the target, 'm_label'
"""# ==============================================================================
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)
def shapeOuts(n):
print ('n={}, hin={},w={}, b={} ,hout={}\n'.format(n, h[n].shape, w[n].shape, b[n].shape, h[n+1]._shape))
def section(n):
with tf.name_scope('section_'+str(n)+'_0') as scope:
w[n]=weight_variable([in_out_width, internal_width],dtype)
b[n]=bias_variable([internal_width],dtype)
h[n+1] = tf.nn.relu(tf.matmul(h[n], w[n]) + b[n])
shapeOuts(n)
with tf.name_scope('section_'+str(n)+'_1') as scope:
w[n+1]=weight_variable([internal_width, internal_width],dtype)
b[n+1]=bias_variable([internal_width],dtype)
h[n+2]=tf.nn.relu(tf.matmul(h[n+1], w[n+1]) + b[n+1])
shapeOuts(n+1)
with tf.name_scope('section_'+str(n)+'_2') as scope:
w[n+2]=weight_variable([internal_width, in_out_width],dtype)
b[n+2]=bias_variable([in_out_width],dtype)
z= tf.nn.relu(tf.matmul(h[n+2], w[n+2]) + b[n+2])
h[n+3]= tf.add(z ,h[n]) #n+3
print('z shape ={}'.format(z._shape))
shapeOuts(n+2)
return
def computeSize(s,tens):
sumC = 1
tShape = tens.get_shape()
nDims = len(tShape)
for i in range(nDims):
sumC *= tShape[i]
print ('\t{}\t{}'.format(s,sumC),flush=True)
return sumC
"""# ==============================================================================
Build sectional network
"""# ==============================================================================
h[0]= self._ph[1]
for i in range(nSections):
section(3*i)
"""# ==============================================================================
Dropout
"""# ==============================================================================
self._keep_prob = tf.placeholder(dtype,name='keep_prob')
with tf.name_scope("drop") as scope:
h_fc2_drop = tf.nn.dropout(h[nSections*3], self._keep_prob)
"""# ==============================================================================
Readout Layer
"""# ==============================================================================
with tf.name_scope("softmax") as scope:
w_fc3 = weight_variable([in_out_width, nTarget],dtype)
b_fc3 = bias_variable([nTarget],dtype)
y_conv=tf.nn.softmax(tf.matmul(h_fc2_drop, w_fc3) + b_fc3)
print ('network size:',flush=True)
total = 0
for i in range(nSections*3):
total = total + computeSize("w{}".format(i),w[i])
total = total + computeSize ("b_fc3",b_fc3) + \
computeSize ("w_fc3",w_fc3)
print('\ttotal\t{}'.format(total),flush=True)
with tf.name_scope("reshape_self._x_image") as scope:
self._x_image = tf.reshape(self._ph.image, [-1,self._nCols,self._nRows,1])
with tf.name_scope("xent") as scope:
# 1e-8 added to eliminate the crash of training when taking log of 0
cross_entropy = -tf.reduce_sum(self._ph[0]*tf.log(y_conv+1e-8))
ce_summ = tf.summary.scalar("cross entropy", cross_entropy)
with tf.name_scope("train") as scope:
self._train_step = tf.train.AdamOptimizer(1e-4).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 Interactive 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])
# To see the results in Chrome,
# Run the following in terminal to activate server.
# tensorboard --logdir '/tmp/ds_logs/'
# See results on localhost:6006
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
def __exit__(self, exc_type, exc_value, traceback):
tf.reset_default_graph() # only necessary when iterating through fonts
self._sess.close()
def reset_graph(self):
tf.reset_default_graph() # only necessary when iterating through fonts
self._sess.close()
#
# def encode(self):
#
# return key
#
# def decode(self, key):