Created
October 24, 2018 15:24
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def NN_model(X, Y, layers_dims, learning_rate = 0.0075, num_iterations = 3000, print_cost=False): | |
costs = [] # keep track of cost | |
parameters = initialize_parameters_deep(layers_dims) | |
# Loop (gradient descent) | |
for i in range(0, num_iterations): | |
# Forward propagation: [LINEAR -> RELU]*(L-1) -> LINEAR -> SIGMOID. | |
AL, caches = L_model_forward(X, parameters) | |
# Compute cost. | |
cost = compute_cost(AL, Y) | |
# Backward propagation. | |
grads = L_model_backward(AL, Y, caches) | |
# Update parameters. | |
parameters = update_parameters(parameters, grads, learning_rate) | |
# Print the cost every 100 training example | |
if print_cost and i % 100 == 0: | |
print ("Cost after iteration %i: %f" %(i, 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 |
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