def alex_net(_X, _weights, _biases, _dropout):
    # Reshape input picture
    _X = tf.reshape(_X, shape=[-1, 28, 28, 1])

    # Convolution Layer
    conv1 = conv2d('conv1', _X, _weights['wc1'], _biases['bc1'])
    # Max Pooling (down-sampling)
    pool1 = max_pool('pool1', conv1, k=2)
    # Apply Normalization
    norm1 = norm('norm1', pool1, lsize=4)
    # Apply Dropout
    norm1 = tf.nn.dropout(norm1, _dropout)

    # Convolution Layer
    conv2 = conv2d('conv2', norm1, _weights['wc2'], _biases['bc2'])
    # Max Pooling (down-sampling)
    pool2 = max_pool('pool2', conv2, k=2)
    # Apply Normalization
    norm2 = norm('norm2', pool2, lsize=4)
    # Apply Dropout
    norm2 = tf.nn.dropout(norm2, _dropout)

    # Convolution Layer
    conv3 = conv2d('conv3', norm2, _weights['wc3'], _biases['bc3'])
    # Max Pooling (down-sampling)
    pool3 = max_pool('pool3', conv3, k=2)
    # Apply Normalization
    norm3 = norm('norm3', pool3, lsize=4)
    # Apply Dropout
    norm3 = tf.nn.dropout(norm3, _dropout)

    # Fully connected layer
    dense1 = tf.reshape(norm3, [-1, _weights['wd1'].get_shape().as_list()[0]]) # Reshape conv3 output to fit dense layer input
    dense1 = tf.nn.relu(tf.matmul(dense1, _weights['wd1']) + _biases['bd1'], name='fc1') # Relu activation

    dense2 = tf.nn.relu(tf.matmul(dense1, _weights['wd2']) + _biases['bd2'], name='fc2') # Relu activation

    # Output, class prediction
    out = tf.matmul(dense2, _weights['out']) + _biases['out']
    return out

# Store layers weight & bias
dataset/ETHPy150Open hpssjellis/easy-tensorflow-on-cloud9/aymericdamien-Examples/examples/alexnet.py/alex_net/original