fashion-mnist-fwdprop.py

def forward_propagation(X, parameters):
    '''
    Implements the forward propagation for the model: 
    LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SOFTMAX
    
    Arguments:
    X -- input dataset placeholder, of shape (input size, number of examples)
    parameters -- python dictionary containing your parameters "W1", "b1", "W2", "b2", "W3", "b3"
                  the shapes are given in initialize_parameters
    Returns:
    Z3 -- the output of the last LINEAR unit
    '''
    
    # Retrieve parameters from dictionary
    W1 = parameters['W1']
    b1 = parameters['b1']
    W2 = parameters['W2']
    b2 = parameters['b2']
    W3 = parameters['W3']
    b3 = parameters['b3']
    
    # Carry out forward propagation      
    Z1 = tf.add(tf.matmul(W1,X), b1)     
    A1 = tf.nn.relu(Z1)                  
    Z2 = tf.add(tf.matmul(W2,A1), b2)    
    A2 = tf.nn.relu(Z2)                  
    Z3 = tf.add(tf.matmul(W3,A2), b3)    
    
    return Z3