coursera吳恩達(dá)的深度學(xué)習(xí)課程 1Neural Networks and Deep Learning第4周作業(yè)：Deep Neural Network - Application錯(cuò)誤和報(bào)錯(cuò)如下：代碼如下：#?GRADED?FUNCTION:?two_layer_model def?two_layer_model(X,?Y,?layers_dims,?learning_rate?=?0.0075,?num_iterations?=?3000,?print_cost=False): ????""" ????Implements?a?two-layer?neural?network:?LINEAR->RELU->LINEAR->SIGMOID. ???? ????Arguments: ????X?--?input?data,?of?shape?(n_x,?number?of?examples) ????Y?--?true?"label"?vector?(containing?0?if?cat,?1?if?non-cat),?of?shape?(1,?number?of?examples) ????layers_dims?--?dimensions?of?the?layers?(n_x,?n_h,?n_y) ????num_iterations?--?number?of?iterations?of?the?optimization?loop ????learning_rate?--?learning?rate?of?the?gradient?descent?update?rule ????print_cost?--?If?set?to?True,?this?will?print?the?cost?every?100?iterations? ???? ????Returns: ????parameters?--?a?dictionary?containing?W1,?W2,?b1,?and?b2 ????""" ???? ????np.random.seed(1) ????impo ????grads?=?{} ????costs?=?[]??????????????????????????????#?to?keep?track?of?the?cost ????m?=?X.shape[1]???????????????????????????#?number?of?examples ????(n_x,?n_h,?n_y)?=?layers_dims ???? ????#?Initialize?parameters?dictionary,?by?calling?one?of?the?functions?you'd?previously?implemented ????###?START?CODE?HERE?###?(≈?1?line?of?code) ????parameters?=?initialize_parameters(n_x,n_h,n_y) ????###?END?CODE?HERE?### ???? ????#?Get?W1,?b1,?W2?and?b2?from?the?dictionary?parameters. ????W1?=?parameters["W1"] ????b1?=?parameters["b1"] ????W2?=?parameters["W2"] ????b2?=?parameters["b2"] ???? ????#?Loop?(gradient?descent) ????for?i?in?range(0,?num_iterations): ????????#?Forward?propagation:?LINEAR?->?RELU?->?LINEAR?->?SIGMOID.?Inputs:?"X,?W1,?b1".?Output:?"A1,?cache1,?A2,?cache2". ????????###?START?CODE?HERE?###?(≈?2?lines?of?code) ????????A1,?cache1?=?linear_activation_forward(X,W1,b1,"relu") ????????A2,?cache2?=?linear_activation_forward(A1,W2,b2,"sigmoid") ????????###?END?CODE?HERE?### ???????? ????????#?Compute?cost ????????###?START?CODE?HERE?###?(≈?1?line?of?code) ????????cost?=?compute_cost(A2,Y) ????????###?END?CODE?HERE?### ???????? ????????#?Initializing?backward?propagation ????????dA2?=?-?(np.divide(Y,?A2)?-?np.divide(1?-?Y,?1?-?A2)) ???????? ????????#?Backward?propagation.?Inputs:?"dA2,?cache2,?cache1".?Outputs:?"dA1,?dW2,?db2;?also?dA0?(not?used),?dW1,?db1". ????????###?START?CODE?HERE?###?(≈?2?lines?of?code) ????????dA1,?dW2,?db2?=?linear_activation_backward(dA2,cache2,"sigmoid") ????????dA0,?dW1,?db1?=?linear_activation_backward(dA1,cache1,"relu" ????????###?END?CODE?HERE?### ???????? ????????#?Set?grads['dWl']?to?dW1,?grads['db1']?to?db1,?grads['dW2']?to?dW2,?grads['db2']?to?db2 ????????grads['dW1']?=?dW1 ????????grads['db1']?=?db1 ????????grads['dW2']?=?dW2 ????????grads['db2']?=?db2 ???????? ????????#?Update?parameters. ????????###?START?CODE?HERE?###?(approx.?1?line?of?code) ????????parameters?=?update_parameters(parameters,grads,learning_rate) ????????###?END?CODE?HERE?### ????????#?Retrieve?W1,?b1,?W2,?b2?from?parameters ????????W1?=?parameters["W1"] ????????b1?=?parameters["b1"] ????????W2?=?parameters["W2"] ????????b2?=?parameters["b2"] ???????? ????????#?Print?the?cost?every?100?training?example ????????if?print_cost?and?i?%?100?==?0: ????????????print("Cost?after?iteration?{}:?{}".format(i,?np.squeeze(cost))) ????????if?print_cost?and?i?%?100?==?0: ????????????costs.append(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() ???? ????return?parameters解決：