TensorFlow Convolution Layer

tf.nn.conv2d() tf.nn.bias_add()



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# Output depth
k_output = 64

# Image Properties
image_width = 10
image_height = 10
color_channels = 3

# Convolution filter
filter_size_width = 5
filter_size_height = 5

# Input/Image
input = tf.placeholder(tf.float32, shape=[None, image_height, image_weight, color_channels])

# Weight and bias
weight = tf.Variable(tf.truncated_normal([filter_size_height, filter_size_width, color_channels, k_output]))
bias = tf.Variable(tf.zeros(k_output))

# Apply Convolution
conv_layer = tf.nn.conv2d(input, weight, strides=[1,2,2,1], padding='SAME')
# Add bias
conv_layer = tf.nn.bias_add(conv_layer, bias)
# Apply activation function
conv_layer = tf.nn.relu(conv_layer)

MaxPooling

tf.nn.max_pool()



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# Apply Max Pooling
conv_layer = tf.nn.max_pool(conv_layer, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
# ksize:      size of the filter. 
# strides:    length of stride. 
# [batch, height, width, channels]