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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):