From 5a8fbb7d19e95f3be16bbee029e82e14f0a240df Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Tue, 22 Aug 2017 00:56:34 -0700 Subject: [PATCH] add data --- python/paddle/v2/framework/tests/mnist.py | 26 +++++++++++++++++------ 1 file changed, 19 insertions(+), 7 deletions(-) diff --git a/python/paddle/v2/framework/tests/mnist.py b/python/paddle/v2/framework/tests/mnist.py index d0c56c457d..f75f196168 100644 --- a/python/paddle/v2/framework/tests/mnist.py +++ b/python/paddle/v2/framework/tests/mnist.py @@ -1,8 +1,9 @@ import paddle.v2.framework.core as core from paddle.v2.framework.op import Operator import numpy +import paddle.v2 as paddle -BATCH_SIZE = 2 +BATCH_SIZE = 100 scope = core.Scope() place = core.CPUPlace() @@ -39,9 +40,9 @@ def feed_data(name, data): tensor = scope.find_var(name).get_tensor() tensor.set_dims(data.shape) if data.dtype == numpy.dtype('int32'): - tensor.alloc_float(place) - elif data.dtype == numpy.dtype('float32'): tensor.alloc_int(place) + elif data.dtype == numpy.dtype('float32'): + tensor.alloc_float(place) else: raise ValueError("data type not supported") tensor.set(data, place) @@ -168,20 +169,31 @@ print(backward_net) optimize_net.complete_add_op(True) print(optimize_net) -PASS_NUM = 10 +reader = paddle.batch( + paddle.reader.shuffle( + paddle.dataset.mnist.train(), buf_size=8192), + batch_size=BATCH_SIZE) + +PASS_NUM = 1000 for pass_id in range(PASS_NUM): print("===========forward==========") - feed_data("pixel", numpy.random.random((BATCH_SIZE, 784)).astype('float32')) - feed_data("label", numpy.ones(BATCH_SIZE).astype("int32")) + # feed_data("pixel", numpy.random.random((BATCH_SIZE, 784)).astype('float32')) + # feed_data("label", numpy.ones(BATCH_SIZE).astype("int32")) + data = reader().next() + 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) forward_network.infer_shape(scope) print_inputs_outputs(forward_network) - print(numpy.array(scope.find_var("label").get_tensor())) + # print(numpy.array(scope.find_var("label").get_tensor())) forward_network.run(scope, dev_ctx) # print(numpy.array(scope.find_var("fc_0").get_tensor())) print("===========backward==========") cost_data = numpy.array(scope.find_var("cross_entropy_1").get_tensor()) + print(cost_data.sum() / len(cost_data)) cost_grad = scope.find_var(grad_var_name("cross_entropy_1")).get_tensor() cost_grad.set_dims(cost_data.shape) cost_grad.alloc_float(place) -- GitLab