SYMBOL INDEX (158 symbols across 12 files)

FILE: batch_normalization.py
  function initialize_parameters (line 9) | def initialize_parameters(layer_dims):
  function relu_forward (line 32) | def relu_forward(Z):
  function sigmoid_forward (line 42) | def sigmoid_forward(Z):
  function linear_forward (line 50) | def linear_forward(X, W, b):
  function batchnorm_forward (line 54) | def batchnorm_forward(z, gamma, beta, epsilon = 1e-12):
  function forward_propagation (line 68) | def forward_propagation(X, parameters, bn_param, decay = 0.9):
  function compute_cost (line 109) | def compute_cost(AL,Y):
  function relu_backward (line 127) | def relu_backward(dA, Z):
  function batchnorm_backward (line 135) | def batchnorm_backward(dout, cache):
  function linear_backward (line 148) | def linear_backward(dZ, cache):
  function backward_propagation (line 160) | def backward_propagation(AL, Y, caches):
  function update_parameters (line 198) | def update_parameters(parameters, grads, learning_rate):
  function random_mini_batches (line 214) | def random_mini_batches(X, Y, mini_batch_size = 64, seed=1):
  function L_layer_model (line 252) | def L_layer_model(X, Y, layer_dims, learning_rate, num_iterations, mini_...
  function forward_propagation_for_test (line 294) | def forward_propagation_for_test(X, parameters, bn_param, epsilon = 1e-12):
  function predict (line 339) | def predict(X_test, y_test, parameters, bn_param):
  function DNN (line 359) | def DNN(X_train, y_train, X_test, y_test, layer_dims, learning_rate= 0.0...

FILE: compare_initializations.py
  function initialize_parameters_zeros (line 7) | def initialize_parameters_zeros(layers_dims):
  function initialize_parameters_random (line 28) | def initialize_parameters_random(layers_dims):
  function initialize_parameters_xavier (line 50) | def initialize_parameters_xavier(layers_dims):
  function initialize_parameters_he (line 72) | def initialize_parameters_he(layers_dims):
  function relu (line 94) | def relu(Z):
  function initialize_parameters (line 104) | def initialize_parameters(layer_dims):
  function forward_propagation (line 117) | def forward_propagation(initialization="he"):

FILE: deep_neural_network_ng.py
  function initialize_parameters (line 6) | def initialize_parameters(layer_dims):
  function linear_forward (line 19) | def linear_forward(A_pre,W,b):
  function relu (line 32) | def relu(Z):
  function sigmoid (line 43) | def sigmoid(Z):
  function linear_activation_forward (line 52) | def linear_activation_forward(A_pre,W,b,activation):
  function L_model_forward (line 71) | def L_model_forward(X,parameters):
  function compute_cost (line 120) | def compute_cost(AL,Y):
  function sigmoid_backward (line 132) | def sigmoid_backward(dA, Z):
  function relu_backward (line 143) | def relu_backward(dA, cache):
  function linear_backward (line 159) | def linear_backward(dZ, cache):
  function linear_activation_backward (line 172) | def linear_activation_backward(dA, cache, activation):
  function L_model_backward (line 189) | def L_model_backward(AL, Y, caches):
  function update_parameters (line 219) | def update_parameters(parameters, grads, learning_rate):
  function L_layer_model (line 233) | def L_layer_model(X, Y, layer_dims, learning_rate, num_iterations):
  function predict (line 266) | def predict(X,y,parameters):
  function DNN (line 286) | def DNN(X_train, y_train, X_test, y_test, layer_dims, learning_rate= 0.0...

FILE: deep_neural_network_release.py
  function initialize_parameters (line 13) | def initialize_parameters(layer_dims):
  function linear_forward (line 29) | def linear_forward(x, w, b):
  function relu_forward (line 39) | def relu_forward(Z):
  function sigmoid (line 49) | def sigmoid(Z):
  function forward_propagation (line 57) | def forward_propagation(X, parameters):
  function compute_cost (line 87) | def compute_cost(AL,Y):
  function relu_backward (line 106) | def relu_backward(dA, Z):
  function linear_backward (line 116) | def linear_backward(dZ, cache):
  function backward_propagation (line 129) | def backward_propagation(AL, Y, caches):
  function update_parameters (line 161) | def update_parameters(parameters, grads, learning_rate):
  function L_layer_model (line 174) | def L_layer_model(X, Y, layer_dims, learning_rate, num_iterations):
  function predict (line 209) | def predict(X_test,y_test,parameters):
  function DNN (line 229) | def DNN(X_train, y_train, X_test, y_test, layer_dims, learning_rate= 0.0...

FILE: deep_neural_network_v1.py
  function initialize_parameters (line 6) | def initialize_parameters(layer_dims):
  function relu (line 18) | def relu(Z):
  function sigmoid (line 27) | def sigmoid(Z):
  function forward_propagation (line 35) | def forward_propagation(X, parameters):
  function compute_cost (line 64) | def compute_cost(AL,Y):
  function relu_backward (line 80) | def relu_backward(Z):
  function backward_propagation (line 88) | def backward_propagation(AL, Y, caches):
  function update_parameters (line 127) | def update_parameters(parameters, grads, learning_rate):
  function L_layer_model (line 140) | def L_layer_model(X, Y, layer_dims, learning_rate, num_iterations):
  function predict (line 173) | def predict(X_test,y_test,parameters):
  function DNN (line 192) | def DNN(X_train, y_train, X_test, y_test, layer_dims, learning_rate= 0.0...

FILE: deep_neural_network_v2.py
  function initialize_parameters (line 6) | def initialize_parameters(layer_dims):
  function relu (line 21) | def relu(Z):
  function sigmoid (line 30) | def sigmoid(Z):
  function forward_propagation (line 38) | def forward_propagation(X, parameters):
  function compute_cost (line 67) | def compute_cost(AL,Y):
  function relu_backward (line 85) | def relu_backward(Z):
  function backward_propagation (line 93) | def backward_propagation(AL, Y, caches):
  function update_parameters (line 132) | def update_parameters(parameters, grads, learning_rate):
  function L_layer_model (line 145) | def L_layer_model(X, Y, layer_dims, learning_rate, num_iterations):
  function predict (line 180) | def predict(X_test,y_test,parameters):
  function DNN (line 199) | def DNN(X_train, y_train, X_test, y_test, layer_dims, learning_rate= 0.0...

FILE: deep_neural_network_with_L2.py
  function initialize_parameters (line 7) | def initialize_parameters(layer_dims):
  function relu (line 23) | def relu(Z):
  function sigmoid (line 32) | def sigmoid(Z):
  function forward_propagation (line 40) | def forward_propagation(X, parameters):
  function compute_cost (line 70) | def compute_cost(AL,Y):
  function compute_cost_with_regularization (line 83) | def compute_cost_with_regularization(AL, Y, parameters, lambd):
  function relu_backward (line 105) | def relu_backward(Z):
  function backward_propagation_with_regularization (line 113) | def backward_propagation_with_regularization(AL, Y, caches, lambd):
  function update_parameters (line 152) | def update_parameters(parameters, grads, learning_rate):
  function L_layer_model (line 165) | def L_layer_model(X, Y, layer_dims, learning_rate, num_iterations,lambd):
  function predict (line 200) | def predict(X_test,y_test,parameters):
  function DNN (line 219) | def DNN(X_train, y_train, X_test, y_test, layer_dims, learning_rate= 0.0...

FILE: deep_neural_network_with_dropout.py
  function initialize_parameters (line 8) | def initialize_parameters(layer_dims):
  function relu (line 24) | def relu(Z):
  function sigmoid (line 33) | def sigmoid(Z):
  function forward_propagation (line 41) | def forward_propagation(X, parameters):
  function forward_propagation_with_dropout (line 72) | def forward_propagation_with_dropout(X, parameters, keep_prob = 0.8):
  function compute_cost (line 108) | def compute_cost(AL,Y):
  function relu_backward (line 121) | def relu_backward(Z):
  function backward_propagation_with_dropout (line 130) | def backward_propagation_with_dropout(AL, Y, caches, keep_prob = 0.8):
  function update_parameters (line 172) | def update_parameters(parameters, grads, learning_rate):
  function L_layer_model (line 185) | def L_layer_model(X, Y, layer_dims, learning_rate, num_iterations,keep_p...
  function predict (line 220) | def predict(X_test,y_test,parameters):
  function DNN (line 240) | def DNN(X_train, y_train, X_test, y_test, layer_dims, learning_rate= 0.0...

FILE: deep_neural_network_with_gd.py
  function initialize_parameters (line 6) | def initialize_parameters(layer_dims):
  function relu (line 21) | def relu(Z):
  function sigmoid (line 30) | def sigmoid(Z):
  function forward_propagation (line 38) | def forward_propagation(X, parameters):
  function compute_cost (line 67) | def compute_cost(AL,Y):
  function relu_backward (line 85) | def relu_backward(Z):
  function backward_propagation (line 93) | def backward_propagation(AL, Y, caches):
  function update_parameters (line 132) | def update_parameters(parameters, grads, learning_rate):
  function random_mini_batches (line 146) | def random_mini_batches(X, Y, mini_batch_size = 64, seed=1):
  function L_layer_model (line 183) | def L_layer_model(X, Y, layer_dims, learning_rate, num_iterations, gradi...
  function predict (line 258) | def predict(X_test,y_test,parameters):
  function DNN (line 277) | def DNN(X_train, y_train, X_test, y_test, layer_dims, learning_rate= 0.0...

FILE: deep_neural_network_with_optimizers.py
  function initialize_parameters (line 6) | def initialize_parameters(layer_dims):
  function relu (line 21) | def relu(Z):
  function sigmoid (line 30) | def sigmoid(Z):
  function forward_propagation (line 38) | def forward_propagation(X, parameters):
  function compute_cost (line 67) | def compute_cost(AL,Y):
  function relu_backward (line 85) | def relu_backward(Z):
  function backward_propagation (line 93) | def backward_propagation(AL, Y, caches):
  function update_parameters_with_gd (line 132) | def update_parameters_with_gd(parameters, grads, learning_rate):
  function random_mini_batches (line 146) | def random_mini_batches(X, Y, mini_batch_size = 64, seed=1):
  function initialize_velocity (line 184) | def initialize_velocity(parameters):
  function update_parameters_with_momentum (line 208) | def update_parameters_with_momentum(parameters, grads, v, beta, learning...
  function update_parameters_with_nesterov_momentum (line 250) | def update_parameters_with_nesterov_momentum(parameters, grads, v, beta,...
  function initialize_adagrad (line 293) | def initialize_adagrad(parameters):
  function update_parameters_with_adagrad (line 317) | def update_parameters_with_adagrad(parameters, grads, G, learning_rate, ...
  function initialize_adadelta (line 359) | def initialize_adadelta(parameters):
  function update_parameters_with_adadelta (line 399) | def update_parameters_with_adadelta(parameters, grads, rho, s, v, delta,...
  function update_parameters_with_rmsprop (line 453) | def update_parameters_with_rmsprop(parameters, grads, s, beta = 0.9, lea...
  function initialize_adam (line 491) | def initialize_adam(parameters):
  function update_parameters_with_adam (line 522) | def update_parameters_with_adam(parameters, grads, v, s, t, learning_rat...
  function L_layer_model (line 569) | def L_layer_model(X, Y, layer_dims, learning_rate, num_iterations, optim...
  function predict (line 637) | def predict(X_test,y_test,parameters):
  function DNN (line 656) | def DNN(X_train, y_train, X_test, y_test, layer_dims, learning_rate= 0.0...

FILE: gradient_checking.py
  function initialize_parameters (line 6) | def initialize_parameters(layer_dims):
  function relu (line 22) | def relu(Z):
  function sigmoid (line 32) | def sigmoid(Z):
  function forward_propagation (line 40) | def forward_propagation(X, parameters):
  function compute_cost (line 70) | def compute_cost(AL,Y):
  function relu_backward (line 84) | def relu_backward(Z):
  function backward_propagation (line 92) | def backward_propagation(AL, Y, caches):
  function dictionary_to_vector (line 132) | def dictionary_to_vector(parameters):
  function gradients_to_vector (line 149) | def gradients_to_vector(gradients):
  function vector_to_dictionary (line 172) | def vector_to_dictionary(theta, layer_dims):
  function gradient_check (line 190) | def gradient_check(parameters, gradients, X, Y, layer_dims, epsilon=1e-7):

FILE: rnn.py
  function initialize_parameters (line 4) | def initialize_parameters(n_a, n_x, n_y):
  function softmax (line 26) | def softmax(x):
  function rnn_step_forward (line 32) | def rnn_step_forward(xt, a_prev, parameters):
  function rnn_forward (line 65) | def rnn_forward(X, Y, a0, parameters, vocab_size=27):
  function rnn_step_backward (line 88) | def rnn_step_backward(dy, gradients, parameters, x, a, a_prev):
  function rnn_backward (line 104) | def rnn_backward(X, Y, parameters, cache):
  function clip (line 127) | def clip(gradients, maxValue):
  function update_parameters (line 151) | def update_parameters(parameters, gradients, lr):
  function sample (line 160) | def sample(parameters, char_to_ix, seed):
  function optimize (line 222) | def optimize(X, Y, a_prev, parameters, learning_rate=0.01):
  function get_initial_loss (line 263) | def get_initial_loss(vocab_size, seq_length):
  function smooth (line 266) | def smooth(loss, cur_loss):
  function print_sample (line 269) | def print_sample(sample_ix, ix_to_char):
  function model (line 275) | def model(data, ix_to_char, char_to_ix, num_iterations=35000, n_a=50, di...