CODEWITHSUNDEEP
pythontensorflowconv-neural-network
Jarades Monteiro
Jarades Monteiro

Reputation: 21

Identify the layers of a Convolutional Neural Network

Do i get this comments right? Are those the 5 layers of my model, as described below?

Model

    # input - conv - conv - linear - linear(fc)
    def model(data): # input Layer

        # 1 conv Layer
        conv = tf.nn.conv2d(data, layer1_weights, [1, 2, 2, 1], padding='SAME')
        hidden = tf.nn.relu(conv + layer1_biases) # Activation function

        # 1 conv Layer
        conv = tf.nn.conv2d(hidden, layer2_weights, [1, 2, 2, 1], padding='SAME')
        hidden = tf.nn.relu(conv + layer2_biases) # Activation function

        # not a layer ( just reshape)
        shape = hidden.get_shape().as_list()
        reshape = tf.reshape(hidden, [shape[0], shape[1] * shape[2] * shape[3]])

        # 1 linear layer - not fc due to relu
        hidden = tf.nn.relu(tf.matmul(reshape, layer3_weights) + layer3_biases)

        # 1 linear fully connected layer
        return tf.matmul(hidden, layer4_weights) + layer4_biases

Upvotes: 1

Views: 276

Answers (2)

collector
collector

Reputation: 990

Your comment labels are right but I think there is an issue with your code.

If you look at the definition of tf.nn.conv2d:

conv2d(
    input,
    filter,
    strides,
    padding,
    use_cudnn_on_gpu=True,
    data_format='NHWC',
    name=None
)

You see that the second argument is not weights but the filter (kernel) shape, defined as: [filter_height, filter_width, in_channels, out_channels]

You could use tf.layers.conv2d instead. It simplifies the code and does the weights, biases and activation in one line. e.g.

conv1 = conv2d(data, filters, kernel_size=[2, 2], padding='same', activation=tf.nn.relu)

Upvotes: 0

Md Shopon
Md Shopon

Reputation: 823

    # 1 linear layer - not fc due to relu
    hidden = tf.nn.relu(tf.matmul(reshape, layer3_weights) + layer3_biases)

In this layer it is a fully connected layer and it is passed through a "RELU" Activation function. The layer of this code is this part 

tf.matmul(reshape, layer3_weights) + layer3_biases

and you are sending this layer through a relu activation function 

tf.nn.relu(tf.matmul(reshape, layer3_weights) + layer3_biases)

Other then this everything seems fine.

Upvotes: 1

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