吳恩達deep learning中實現CNN的小作業

04-05

最近在刷吳恩達deep learning的課程，正好學到了第四課完成了一個小作業就想在知乎上面記錄一下，覺得作業中的注釋很清楚，很適合學習。

話不多說，上作業。

import mathimport numpy as npimport h5pyimport matplotlib.pyplot as pltimport scipyfrom PIL import Imagefrom scipy import ndimageimport tensorflow as tffrom tensorflow.python.framework import opsfrom cnn_utils import *%matplotlib inlinenp.random.seed(1)# Loading the data (signs)X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()# Example of a pictureindex = 6plt.imshow(X_train_orig[index])print ("y = " + str(np.squeeze(Y_train_orig[:, index])))# examine the shapes of dataX_train = X_train_orig/255.X_test = X_test_orig/255.Y_train = convert_to_one_hot(Y_train_orig, 6).TY_test = convert_to_one_hot(Y_test_orig, 6).Tprint ("number of training examples = " + str(X_train.shape[0]))print ("number of test examples = " + str(X_test.shape[0]))print ("X_train shape: " + str(X_train.shape))print ("Y_train shape: " + str(Y_train.shape))print ("X_test shape: " + str(X_test.shape))print ("Y_test shape: " + str(Y_test.shape))conv_layers = {}# GRADED FUNCTION: create_placeholdersdef create_placeholders(n_H0, n_W0, n_C0, n_y): """ Creates the placeholders for the tensorflow session. Arguments: n_H0 -- scalar, height of an input image n_W0 -- scalar, width of an input image n_C0 -- scalar, number of channels of the input n_y -- scalar, number of classes Returns: X -- placeholder for the data input, of shape [None, n_H0, n_W0, n_C0] and dtype "float" Y -- placeholder for the input labels, of shape [None, n_y] and dtype "float" """ X = tf.placeholder(shape = [None,n_H0,n_W0,n_C0],dtype = tf.float32) Y = tf.placeholder(shape = [None,n_y],dtype = tf.float32) return X, Y# GRADED FUNCTION: initialize_parametersdef initialize_parameters(): """ Initializes weight parameters to build a neural network with tensorflow. The shapes are: W1 : [4, 4, 3, 8] W2 : [2, 2, 8, 16] Returns: parameters -- a dictionary of tensors containing W1, W2 """ tf.set_random_seed(1) # so that your "random" numbers match ours W1 = tf.get_variable("W1",[4,4,3,8],initializer = tf.contrib.layers.xavier_initializer(seed = 0) ) W2 = tf.get_variable("W2",[2,2,8,16],initializer = tf.contrib.layers.xavier_initializer(seed = 0)) parameters = {"W1": W1, "W2": W2} return parametersdef forward_propagation(X, parameters): """ Implements the forward propagation for the model: CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED Arguments: X -- input dataset placeholder, of shape (input size, number of examples) parameters -- python dictionary containing your parameters "W1", "W2" the shapes are given in initialize_parameters Returns: Z3 -- the output of the last LINEAR unit """ # Retrieve the parameters from the dictionary "parameters" W1 = parameters[W1] W2 = parameters[W2] # CONV2D Z1 = tf.nn.conv2d(X,W1, strides = [1,1,1,1], padding = SAME) # RELU A1 = tf.nn.relu(Z1) # MAXPOOL P1 = tf.nn.max_pool(A1, ksize = [1,8,8,1], strides = [1,8,8,1], padding = SAME) # CONV2D Z2 = tf.nn.conv2d(P1,W2, strides = [1,1,1,1], padding = SAME) # RELU A2 = tf.nn.relu(Z2) # MAXPOOL P2 = tf.nn.max_pool(A2, ksize = [1,4,4,1], strides = [1,4,4,1], padding = SAME) # FLATTEN P2 = tf.contrib.layers.flatten(P2) # FULLY-CONNECTED without non-linear activation function (not not call softmax). Z3 = tf.contrib.layers.fully_connected(P2, 6, activation_fn=None) return Z3# GRADED FUNCTION: compute_costdef compute_cost(Z3, Y): """ Computes the cost Arguments: Z3 -- output of forward propagation (output of the last LINEAR unit), of shape (6, number of examples) Y -- "true" labels vector placeholder, same shape as Z3 Returns: cost - Tensor of the cost function """ cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = Z3, labels = Y)) return cost# GRADED FUNCTION: modeldef model(X_train, Y_train, X_test, Y_test, learning_rate = 0.009, num_epochs = 100, minibatch_size = 64, print_cost = True): """ Implements a three-layer ConvNet in Tensorflow: CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED Arguments: X_train -- training set, of shape (None, 64, 64, 3) Y_train -- test set, of shape (None, n_y = 6) X_test -- training set, of shape (None, 64, 64, 3) Y_test -- test set, of shape (None, n_y = 6) learning_rate -- learning rate of the optimization num_epochs -- number of epochs of the optimization loop minibatch_size -- size of a minibatch print_cost -- True to print the cost every 100 epochs Returns: train_accuracy -- real number, accuracy on the train set (X_train) test_accuracy -- real number, testing accuracy on the test set (X_test) 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 results consistent (tensorflow seed) seed = 3 # to keep results consistent (numpy seed) (m, n_H0, n_W0, n_C0) = X_train.shape n_y = Y_train.shape[1] costs = [] # To keep track of the cost # Create Placeholders of the correct shape X, Y = create_placeholders(n_H0, n_W0, n_C0, n_y) # Initialize parameters parameters = initialize_parameters() # Forward propagation: Build the forward propagation in the tensorflow graph Z3 = forward_propagation(X, parameters) # Cost function: Add cost function to tensorflow graph cost = compute_cost(Z3, Y) # Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer that minimizes the cost. optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost) # Initialize all the variables globally init = tf.global_variables_initializer() # Start the session to 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): minibatch_cost = 0. num_minibatches = int(m / minibatch_size) # number of minibatches of size minibatch_size in the train set 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 # IMPORTANT: The line that runs the graph on a minibatch. # Run the session to execute the optimizer and the cost, the feedict should contain a minibatch for (X,Y). _ , temp_cost = sess.run([optimizer, cost] , feed_dict={X: minibatch_X, Y: minibatch_Y}) minibatch_cost += temp_cost / num_minibatches # Print the cost every epoch if print_cost == True and epoch % 5 == 0: print ("Cost after epoch %i: %f" % (epoch, minibatch_cost)) if print_cost == True and epoch % 1 == 0: costs.append(minibatch_cost) # plot the cost plt.plot(np.squeeze(costs)) plt.ylabel(cost) plt.xlabel(iterations (per tens)) plt.title("Learning rate =" + str(learning_rate)) plt.show() # Calculate the correct predictions predict_op = tf.argmax(Z3, 1) correct_prediction = tf.equal(predict_op, tf.argmax(Y, 1)) # Calculate accuracy on the test set accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) print(accuracy) train_accuracy = accuracy.eval({X: X_train, Y: Y_train}) test_accuracy = accuracy.eval({X: X_test, Y: Y_test}) print("Train Accuracy:", train_accuracy) print("Test Accuracy:", test_accuracy) return train_accuracy, test_accuracy, parameters_, _, parameters = model(X_train, Y_train, X_test, Y_test)