convnet.py

    # Convolutional layer 1
    with tf.name_scope('conv1'):
        W = tf.Variable(
            tf.truncated_normal(
                shape=(
                    CONV1_FILTER_SIZE, 
                    CONV1_FILTER_SIZE,
                    NUM_CHANNELS,
                    CONV1_FILTER_COUNT),
                dtype=tf.float32,
                stddev=5e-2), 
            name='weights')
        b = tf.Variable(
            tf.zeros(
                shape=(CONV1_FILTER_COUNT), 
                dtype=tf.float32), 
            name='biases')
        conv = tf.nn.conv2d(
            input=images, 
            filter=W,
            strides=(1, 1, 1, 1), 
            padding='SAME',
            name='convolutional')
        conv_bias = tf.nn.bias_add(conv, b)
        conv_act = tf.nn.relu(
            features=conv_bias, 
            name='activation')
        pool1 = tf.nn.max_pool(
            value=conv_act, 
            ksize=(1, 2, 2, 1), 
            strides=(1, 2, 2, 1), 
            padding='SAME', 
            name='subsampling')

    # Convolutional layer 2
    with tf.name_scope('conv2'):
        W = tf.Variable(
            tf.truncated_normal(
                shape=(
                    CONV2_FILTER_SIZE, 
                    CONV2_FILTER_SIZE,
                    CONV1_FILTER_COUNT,
                    CONV2_FILTER_COUNT),
                dtype=tf.float32,
                stddev=5e-2), 
            name='weights')
        b = tf.Variable(
            tf.zeros(
                shape=(CONV2_FILTER_COUNT), 
                dtype=tf.float32), 
            name='biases')
        conv = tf.nn.conv2d(
            input=pool1, 
            filter=W,
            strides=(1, 1, 1, 1), 
            padding='SAME',
            name='convolutional')
        conv_bias = tf.nn.bias_add(conv, b)
        conv_act = tf.nn.relu(
            features=conv_bias, 
            name='activation')
        pool2 = tf.nn.max_pool(
            value=conv_act, 
            ksize=(1, 2, 2, 1), 
            strides=(1, 2, 2, 1), 
            padding='SAME', 
            name='subsampling')

    # Hidden layer
    with tf.name_scope('hidden'):
        conv_output_size = 28800 
        W = tf.Variable(
            tf.truncated_normal(
                shape=(conv_output_size, HIDDEN_LAYER_SIZE), 
                dtype=tf.float32,
                stddev=5e-2), 
            name='weights')
        b = tf.Variable(
            tf.zeros(
                shape=(HIDDEN_LAYER_SIZE), 
                dtype=tf.float32), 
            name='biases')
        reshape = tf.reshape(
            tensor=pool2, 
            shape=[BATCH_SIZE, -1])
        h1 = tf.nn.relu(
            features=tf.add(tf.matmul(reshape, W), b),
            name='activation')

    # Softmax layer
    with tf.name_scope('softmax'):
        W = tf.Variable(
            tf.truncated_normal(
                shape=(HIDDEN_LAYER_SIZE, NUM_CLASS), 
                dtype=tf.float32,
                stddev=5e-2),
            name='weights')
        b = tf.Variable(
            tf.zeros(
                shape=(NUM_CLASS), 
                dtype=tf.float32), 
            name='biases')
        logits = tf.add(tf.matmul(h1, W), b, name='logits')