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vggnet_normal.py
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import os
import tensorflow as tf
import tensorflow.keras.layers as nn
class vggnet(tf.keras.Model):
def __init__(self, classes=1000):
super(vggnet, self).__init__()
self.conv1 = nn.Conv2D(
filters=96,
kernel_size=7,
strides=2,
padding='same',
activation=None,
use_bias=False)
self.pool1 = nn.MaxPool2D(pool_size=2, strides=2)
self.bn1 = nn.BatchNormalization(center=False, scale=False)
self.conv2 = nn.Conv2D(
filters=256,
kernel_size=3,
strides=1,
padding='same',
activation='relu',
use_bias=False)
self.bn2 = nn.BatchNormalization()
self.conv3 = nn.Conv2D(
filters=256,
kernel_size=3,
strides=1,
padding='same',
activation='relu',
use_bias=False)
self.bn3 = nn.BatchNormalization()
self.conv4 = nn.Conv2D(
filters=256,
kernel_size=3,
strides=1,
padding='same',
activation='relu',
use_bias=False)
self.pool4 = nn.MaxPool2D(pool_size=2, strides=2)
self.bn4 = nn.BatchNormalization()
self.conv5 = nn.Conv2D(
filters=512,
kernel_size=3,
strides=1,
padding='same',
activation='relu',
use_bias=False)
self.bn5 = nn.BatchNormalization()
self.conv6 = nn.Conv2D(
filters=512,
kernel_size=3,
strides=1,
padding='same',
activation='relu',
use_bias=False)
self.bn6 = nn.BatchNormalization()
self.conv7 = nn.Conv2D(
filters=512,
kernel_size=3,
strides=1,
padding='same',
activation='relu',
use_bias=False)
self.pool7 = nn.MaxPool2D(pool_size=2, strides=2)
self.bn7 = nn.BatchNormalization()
self.conv8 = nn.Conv2D(
filters=512,
kernel_size=3,
strides=1,
padding='same',
activation='relu',
use_bias=False)
self.bn8 = nn.BatchNormalization()
self.conv9 = nn.Conv2D(
filters=512,
kernel_size=3,
strides=1,
padding='same',
activation='relu',
use_bias=False)
self.bn9 = nn.BatchNormalization()
self.conv10 = nn.Conv2D(
filters=512,
kernel_size=3,
strides=1,
padding='same',
activation='relu',
use_bias=False)
self.pool10 = nn.MaxPool2D(pool_size=2, strides=2)
self.bn10 = nn.BatchNormalization()
self.flatten = nn.Flatten()
self.dense1 = nn.Dense(4096, use_bias=False, activation='relu')
self.bn11 = nn.BatchNormalization()
self.dense2 = nn.Dense(4096, use_bias=False, activation='relu')
self.bn12 = nn.BatchNormalization()
self.dense3 = nn.Dense(classes, use_bias=False)
self.softmax = nn.Activation('softmax')
def call(self, inputs, training=None, debug=False):
layers = []
with tf.name_scope('unbinarized'):
x = self.conv1(inputs)
layers.append(x)
x = self.pool1(x)
layers.append(x)
x = self.bn1(x, training=training)
layers.append(x)
x = self.conv2(x)
layers.append(x)
x = self.bn2(
x, training=training)
layers.append(x)
x = self.conv3(x)
layers.append(x)
x = self.bn3(
x, training=training)
layers.append(x)
x = self.conv4(x)
layers.append(x)
x = self.pool4(x)
layers.append(x)
x = self.bn4(
x, training=training)
layers.append(x)
x = self.conv5(x)
layers.append(x)
x = self.bn5(
x, training=training)
layers.append(x)
x = self.conv6(x)
layers.append(x)
x = self.bn6(
x, training=training)
layers.append(x)
x = self.conv7(x)
layers.append(x)
x = self.pool7(x)
layers.append(x)
x = self.bn7(
x, training=training)
layers.append(x)
x = self.conv8(x)
layers.append(x)
x = self.bn8(
x, training=training)
layers.append(x)
x = self.conv9(x)
layers.append(x)
x = self.bn9(
x, training=training)
layers.append(x)
x = self.conv10(x)
layers.append(x)
x = self.pool10(x)
layers.append(x)
x = self.bn10(
x, training=training)
layers.append(x)
x = self.flatten(x)
layers.append(x)
x = self.dense1(x)
layers.append(x)
x = self.bn11(
x, training=training)
layers.append(x)
x = self.dense2(x)
layers.append(x)
x = self.bn12(
x, training=training)
layers.append(x)
x = self.dense3(x)
layers.append(x)
x = self.softmax(x)
tf.compat.v1.summary.histogram('output', x)
if debug:
return layers
else:
return x