week 2 assignments

deep-learning/4-convolutional-neural-networks/2-deep-convolutional-models-case-studies/KerasTutorial/kt_utils.py

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+import keras.backend as K
+import math
+import numpy as np
+import h5py
+import matplotlib.pyplot as plt
+
+
+def mean_pred(y_true, y_pred):
+    return K.mean(y_pred)
+
+def load_dataset():
+    train_dataset = h5py.File('datasets/train_happy.h5', "r")
+    train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features
+    train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels
+
+    test_dataset = h5py.File('datasets/test_happy.h5', "r")
+    test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set features
+    test_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labels
+
+    classes = np.array(test_dataset["list_classes"][:]) # the list of classes
+
+    train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
+    test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
+
+    return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes
+

deep-learning/4-convolutional-neural-networks/2-deep-convolutional-models-case-studies/ResNets/resnets_utils.py

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+import os
+import numpy as np
+import tensorflow as tf
+import h5py
+import math
+
+def load_dataset():
+    train_dataset = h5py.File('datasets/train_signs.h5', "r")
+    train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features
+    train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels
+
+    test_dataset = h5py.File('datasets/test_signs.h5', "r")
+    test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set features
+    test_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labels
+
+    classes = np.array(test_dataset["list_classes"][:]) # the list of classes
+
+    train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
+    test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
+
+    return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes
+
+
+def random_mini_batches(X, Y, mini_batch_size = 64, seed = 0):
+    """
+    Creates a list of random minibatches from (X, Y)
+    
+    Arguments:
+    X -- input data, of shape (input size, number of examples) (m, Hi, Wi, Ci)
+    Y -- true "label" vector (containing 0 if cat, 1 if non-cat), of shape (1, number of examples) (m, n_y)
+    mini_batch_size - size of the mini-batches, integer
+    seed -- this is only for the purpose of grading, so that you're "random minibatches are the same as ours.
+    
+    Returns:
+    mini_batches -- list of synchronous (mini_batch_X, mini_batch_Y)
+    """
+
+    m = X.shape[0]                  # number of training examples
+    mini_batches = []
+    np.random.seed(seed)
+
+    # Step 1: Shuffle (X, Y)
+    permutation = list(np.random.permutation(m))
+    shuffled_X = X[permutation,:,:,:]
+    shuffled_Y = Y[permutation,:]
+
+    # Step 2: Partition (shuffled_X, shuffled_Y). Minus the end case.
+    num_complete_minibatches = math.floor(m/mini_batch_size) # number of mini batches of size mini_batch_size in your partitionning
+    for k in range(0, num_complete_minibatches):
+        mini_batch_X = shuffled_X[k * mini_batch_size : k * mini_batch_size + mini_batch_size,:,:,:]
+        mini_batch_Y = shuffled_Y[k * mini_batch_size : k * mini_batch_size + mini_batch_size,:]
+        mini_batch = (mini_batch_X, mini_batch_Y)
+        mini_batches.append(mini_batch)
+
+    # Handling the end case (last mini-batch < mini_batch_size)
+    if m % mini_batch_size != 0:
+        mini_batch_X = shuffled_X[num_complete_minibatches * mini_batch_size : m,:,:,:]
+        mini_batch_Y = shuffled_Y[num_complete_minibatches * mini_batch_size : m,:]
+        mini_batch = (mini_batch_X, mini_batch_Y)
+        mini_batches.append(mini_batch)
+
+    return mini_batches
+
+
+def convert_to_one_hot(Y, C):
+    Y = np.eye(C)[Y.reshape(-1)].T
+    return Y
+
+
+def forward_propagation_for_predict(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 the parameters from the dictionary "parameters" 
+    W1 = parameters['W1']
+    b1 = parameters['b1']
+    W2 = parameters['W2']
+    b2 = parameters['b2']
+    W3 = parameters['W3']
+    b3 = parameters['b3'] 
+                                                           # Numpy Equivalents:
+    Z1 = tf.add(tf.matmul(W1, X), b1)                      # Z1 = np.dot(W1, X) + b1
+    A1 = tf.nn.relu(Z1)                                    # A1 = relu(Z1)
+    Z2 = tf.add(tf.matmul(W2, A1), b2)                     # Z2 = np.dot(W2, a1) + b2
+    A2 = tf.nn.relu(Z2)                                    # A2 = relu(Z2)
+    Z3 = tf.add(tf.matmul(W3, A2), b3)                     # Z3 = np.dot(W3,Z2) + b3
+
+    return Z3
+
+def predict(X, parameters):
+
+    W1 = tf.convert_to_tensor(parameters["W1"])
+    b1 = tf.convert_to_tensor(parameters["b1"])
+    W2 = tf.convert_to_tensor(parameters["W2"])
+    b2 = tf.convert_to_tensor(parameters["b2"])
+    W3 = tf.convert_to_tensor(parameters["W3"])
+    b3 = tf.convert_to_tensor(parameters["b3"])
+
+    params = {"W1": W1,
+              "b1": b1,
+              "W2": W2,
+              "b2": b2,
+              "W3": W3,
+              "b3": b3}
+
+    x = tf.placeholder("float", [12288, 1])
+
+    z3 = forward_propagation_for_predict(x, params)
+    p = tf.argmax(z3)
+
+    sess = tf.Session()
+    prediction = sess.run(p, feed_dict = {x: X})
+
+    return prediction