# Build a convolutional neural network
def conv_net(x, n_classes, dropout, reuse, is_training):
    # Define a scope for reusing the variables
    with tf.variable_scope('ConvNet', reuse=reuse):
        # MNIST data input is a 1-D vector of 784 features (28*28 pixels)
        # Reshape to match picture format [Height x Width x Channel]
        # Tensor input become 4-D: [Batch Size, Height, Width, Channel]
        x = tf.reshape(x, shape=[-1, 28, 28, 1])

        # Convolution Layer with 64 filters and a kernel size of 5
        x = tf.layers.conv2d(x, 64, 5, activation=tf.nn.relu)
        # Max Pooling (down-sampling) with strides of 2 and kernel size of 2
        x = tf.layers.max_pooling2d(x, 2, 2)

        # Convolution Layer with 256 filters and a kernel size of 5
        x = tf.layers.conv2d(x, 256, 3, activation=tf.nn.relu)
        # Convolution Layer with 512 filters and a kernel size of 5
        x = tf.layers.conv2d(x, 512, 3, activation=tf.nn.relu)
        # Max Pooling (down-sampling) with strides of 2 and kernel size of 2
        x = tf.layers.max_pooling2d(x, 2, 2)

        # Flatten the data to a 1-D vector for the fully connected layer
        x = tf.contrib.layers.flatten(x)

        # Fully connected layer (in contrib folder for now)
        x = tf.layers.dense(x, 2048)
        # Apply Dropout (if is_training is False, dropout is not applied)
        x = tf.layers.dropout(x, rate=dropout, training=is_training)

        # Fully connected layer (in contrib folder for now)
        x = tf.layers.dense(x, 1024)
        # Apply Dropout (if is_training is False, dropout is not applied)
        x = tf.layers.dropout(x, rate=dropout, training=is_training)

        # Output layer, class prediction
        out = tf.layers.dense(x, n_classes)
        # Because 'softmax_cross_entropy_with_logits' loss already apply
        # softmax, we only apply softmax to testing network
        out = tf.nn.softmax(out) if not is_training else out

    return out