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#import tensorflow as tf

from tensorflow.compat import v1 as tf

tf.compat.v1.disable_eager_execution()

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', '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

nFc = 1024 # 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(0.1, 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_fc1 = weight_variable([n_h_pool2_outputsx, nFc],dtype)

b_fc1 = bias_variable([nFc],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 ):

print(i)

print(self._ph[i])

h_pool2_flat = tf.concat([h_pool2_flat, self._ph[i]],1)

h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

"""# ==============================================================================

Dropout

"""# ==============================================================================

with tf.name_scope("drop") as scope:

h_fc1_drop = tf.nn.dropout(h_fc1, self._keep_prob)

"""# ==============================================================================

Readout Layer

"""# ==============================================================================

with tf.name_scope("softmax") as scope:

W_fc2 = weight_variable([nFc, nTarget],dtype)

b_fc2 = bias_variable([nTarget],dtype)

y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

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)

weight_summary = tf.summary.histogram("weights", W_fc2)

"""# ==============================================================================

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])

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]

print ('\t{}\t{}'.format(s,sumC),flush=True)

return sumC

print ('network size:',flush=True)

total = computeSize("W_fc1",W_fc1)+ \

computeSize ("b_fc1",b_fc1) + \

computeSize ("W_conv1",W_conv1) + \

computeSize ("b_conv1",b_conv1) + \

computeSize ("W_conv2",W_conv2) + \

computeSize ("b_conv2",b_conv2) + \

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()