From cf515e4a72f4b02fbbbfdbd79c3b66b1be694e7b Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Wed, 23 Aug 2017 09:39:47 -0700 Subject: [PATCH] optimize code and name --- python/paddle/v2/framework/tests/mnist.py | 56 +++++++++++------------ 1 file changed, 27 insertions(+), 29 deletions(-) diff --git a/python/paddle/v2/framework/tests/mnist.py b/python/paddle/v2/framework/tests/mnist.py index e47de2436ff..886e99610dd 100644 --- a/python/paddle/v2/framework/tests/mnist.py +++ b/python/paddle/v2/framework/tests/mnist.py @@ -134,7 +134,7 @@ def cross_entropy_layer(net, input, label): return cost_name -def get_backward_net(forward_net): +def create_backward_net(forward_net): net = core.Operator.backward(forward_net, set()) for input in net.inputs()["all"]: var = scope.new_var(input) @@ -145,29 +145,29 @@ def get_backward_net(forward_net): return net -def print_inputs_outputs(op): +def debug_print_op(op): print("===============" + op.type() + "==============") print("***inputs:***") for input in op.inputs()["all"]: print input, scope.find_var(input).get_tensor().get_dims() - print("***outputs:***") + print("\n***outputs:***") for output in op.outputs()["all"]: print output, scope.find_var(output).get_tensor().get_dims() print("") print("") -def set_cost(): - cost_shape = numpy.array(scope.find_var("cross_entropy_3").get_tensor( - )).shape - cost_grad = scope.find_var(grad_var_name("cross_entropy_3")).get_tensor() +def set_cost(cost): + cost_shape = numpy.array(scope.find_var(cost).get_tensor()).shape + cost_grad = \ + scope.find_var(grad_var_name(cost)).get_tensor() cost_grad.set_dims(cost_shape) cost_grad.alloc_float(place) cost_grad.set(numpy.ones(cost_shape).astype("float32"), place) -def mean_cost(): - cost_data = numpy.array(scope.find_var("cross_entropy_3").get_tensor()) +def mean_cost(cost): + cost_data = numpy.array(scope.find_var(cost).get_tensor()) return cost_data.sum() / len(cost_data) @@ -180,23 +180,23 @@ def error_rate(predict, label): images = data_layer(name='pixel', dims=[BATCH_SIZE, 784]) -label = data_layer(name='label', dims=[BATCH_SIZE]) +labels = data_layer(name='label', dims=[BATCH_SIZE]) fc1 = fc_layer(net=forward_network, input=images, size=100, act="sigmoid") fc2 = fc_layer(net=forward_network, input=fc1, size=100, act="sigmoid") predict = fc_layer(net=forward_network, input=fc2, size=100, act="softmax") -cost = cross_entropy_layer(net=forward_network, input=predict, label=label) +cost = cross_entropy_layer(net=forward_network, input=predict, label=labels) forward_network.complete_add_op(True) -backward_net = get_backward_net(forward_network) +backward_net = create_backward_net(forward_network) optimize_net.complete_add_op(True) print(forward_network) print(backward_net) print(optimize_net) -print_inputs_outputs(forward_network) -print_inputs_outputs(backward_net) -print_inputs_outputs(optimize_net) +debug_print_op(forward_network) +debug_print_op(backward_net) +debug_print_op(optimize_net) train_reader = paddle.batch( paddle.reader.shuffle( @@ -204,19 +204,19 @@ train_reader = paddle.batch( batch_size=BATCH_SIZE) -def test(): +def test(cost_name): test_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=128) cost = [] error = [] for data in test_reader(): - image = numpy.array(map(lambda x: x[0], data)).astype("float32") - label = numpy.array(map(lambda x: x[1], data)).astype("int32") - feed_data("pixel", image) - feed_data("label", label) + image_data = numpy.array(map(lambda x: x[0], data)).astype("float32") + label_data = numpy.array(map(lambda x: x[1], data)).astype("int32") + feed_data(images, image_data) + feed_data(labels, label_data) forward_network.infer_shape(scope) forward_network.run(scope, dev_ctx) - cost.append(mean_cost()) + cost.append(mean_cost(cost_name)) error.append(error_rate(predict, "label")) print("cost=" + str(sum(cost) / float(len(cost))) + " error_rate=" + str( sum(error) / float(len(error)))) @@ -227,22 +227,20 @@ for pass_id in range(PASS_NUM): batch_id = 0 for data in train_reader(): - image = numpy.array(map(lambda x: x[0], data)).astype("float32") - label = numpy.array(map(lambda x: x[1], data)).astype("int32") - feed_data("pixel", image) - feed_data("label", label) + image_data = numpy.array(map(lambda x: x[0], data)).astype("float32") + label_data = numpy.array(map(lambda x: x[1], data)).astype("int32") + feed_data(images, image_data) + feed_data(labels, label_data) forward_network.infer_shape(scope) forward_network.run(scope, dev_ctx) - set_cost() + set_cost(cost) backward_net.infer_shape(scope) backward_net.run(scope, dev_ctx) optimize_net.run(scope, dev_ctx) if batch_id % 100 == 0: print("pass[" + str(pass_id) + "] batch_id[" + str(batch_id) + "]") - test() - # print(mean_cost()) - # print(error_rate(predict, "label")) + test(cost) batch_id = batch_id + 1 -- GitLab