def L_layer_model(X,
Y,
layers_dims,
learning_rate=0.5,
num_iterations=10000,
print_cost=False): #lr was 0.009
np.random.seed(1)
costs = []
parameters = functions.initialize_parameters_deep(layers_dims)
for i in range(0, num_iterations):
AL, caches = functions.L_model_forward(X, parameters)
cost = functions.compute_cost(AL, Y)
grads = functions.L_model_backward(AL, Y, caches)
parameters = functions.update_parameters(parameters, grads,
learning_rate)
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)
#plt.ylabel('cost')
#plt.xlabel('iterations (per tens)')
#plt.title("Learning rate =" + str(learning_rate))
#plt.show()
return parameters
def L_layer_model(
X, y, layers_dims, learning_rate=0.01, num_iterations=3000,
print_cost=True, hidden_layers_activation_fn="relu"):
random.seed(version =2)
np.random.seed(random.randint(0,1000))
parameters = fn.initialize_parameters(layers_dims)
for i in range(num_iterations):
AL, caches = fn.L_model_forward(
X, parameters, hidden_layers_activation_fn)
cost = fn.compute_cost(AL, y)
grads = fn.L_model_backward(AL, y, caches, hidden_layers_activation_fn)
parameters = fn.update_parameters(parameters, grads, learning_rate)
if (i + 1) % 100 == 0 and print_cost:
print(f"The cost after {i + 1} iterations is: {cost:.4f}")
return parameters