signs_model1.py

def model(X_train, Y_train, X_test, Y_test, learning_rate = 0.0001, num_epochs = 1500, minibatch_size = 32, print_cost = True):
    """
    Implements a three-layer tensorflow neural network: LINEAR->RELU->LINEAR->RELU->LINEAR->SOFTMAX.
    Returns:
    parameters -- parameters learnt by the model. They can then be used to predict.
    """
    ops.reset_default_graph() # to be able to rerun the model without overwriting tf variables
    tf.set_random_seed(1)            # to keep a consistent result
    seed = 3                         # to keep a consistent result, used in mini-batches
    (n_x, m) = X_train.shape         # n_x : input size (input features); m : num of examples in the train set
    n_y = Y_train.shape[0]           # n_y : num of classes
    costs = []
    
    # Create Placeholders
    X, Y = create_placeholders(n_x, n_y)
    
    # Initialize Parameters
    parameters = initialize_parameters()
    
    # Forward Propagation
    Z3 = forward_propagation(X, parameters)
    
    # Cost Function
    cost = compute_cost(Z3, Y)
    
    # Backpropagation using Adam optimizer
    optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(cost)
    
    # initialize all the variables
    init = tf.global_variables_initializer()
    
    # start the session and compute the tensorflow graph
    with tf.Session() as sess:
        # run the initialization
        sess.run(init)
        
        # do the training loop 
        for epoch in range(num_epochs):
            epoch_cost = 0.       # defines a cost related to an epoch
            num_minibatches = int(m / minibatch_size)
            seed = seed + 1
            minibatches = random_mini_batches(X_train, Y_train, minibatch_size, seed)
            
            for minibatch in minibatches:
                # select a minibatch
                (minibatch_X, minibatch_Y) = minibatch
                # optimization
                _, minibatch_cost = sess.run([optimizer, cost], feed_dict = {X : minibatch_X, Y : minibatch_Y})
                # compute the epoch cost
                epoch_cost += minibatch_cost / num_minibatches
                
            # print the cost
            if print_cost == True and epoch % 100 == 0:
                print("Cost after epoch %i: %f" % (epoch, epoch_cost))
            if print_cost == True and epoch % 5 == 0:
                costs.append(epoch_cost)
                
        # Plot the cost - 
        plt.plot(np.squeeze(costs))
        plt.ylabel('cost')
        plt.xlabel('iterations (per fives)')
        plt.title("Learning Rate = " + str(learning_rate))
        plt.show()
        
        # saving parameters in a variable
        parameters = sess.run(parameters)
        print("Parameters have been trained")
        
        # calculate the correct predictions
        correct_prediction = tf.equal(tf.argmax(Z3), tf.argmax(Y))
        
        # calculate accuracy on the test set
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
        
        print("Train Accuracy: ", accuracy.eval({X : X_train, Y : Y_train}))
        print("Test Accuracy: ", accuracy.eval({X : X_test, Y : Y_test}))
        
        return parameters