mnist.py

import paddle.v2.framework.core as core
from paddle.v2.framework.op import Operator
import numpy

BATCH_SIZE = 100

scope = core.Scope()
place = core.CPUPlace()
dev_ctx = core.DeviceContext.create(place)

# init_net = core.Net.create()
forward_network = core.Net.create()

# should be init after forward_op is constructed
# backward_net = core.Operator.backward(forward_net, set())
backward_net = None
optimize_net = core.Net.create()


def atom_id():
    id = 0
    while True:
        yield id
        id += 1


uniq_id = atom_id().next


def data_layer(name, dims):
    var = scope.new_var(name)
    tensor = var.get_tensor()
    tensor.set_dims(dims)  # 1 is batch size holder.
    return name


def feed_data(name, data):
    assert isinstance(data, numpy.array)
    tensor = scope.find_var(name).get_tensor()
    tensor.set_dims(data.shape)
    tensor.alloc_float(place)
    tensor.set(data, place)


def grad_var_name(var_name):
    return var_name + "@GRAD"


def sgd_optimizer(net, param_name, learning_rate=0.01):
    grad_name = grad_var_name(param_name)
    optimize_op = Operator(
        "sgd", param=param_name, grad=grad_name, learning_rate=learning_rate)
    net.add_op(optimize_op)


# should use operator and add these to the init_network
def init_param(param_name, dims):
    print param_name
    var = scope.new_var(param_name)
    tensor = var.get_tensor()
    tensor.set_dims(dims)
    data = numpy.random.uniform(
        low=0.0, high=1.0, size=tensor.shape()).astype("float32")
    tensor.set(data, place)


# fc_layer
def fc_layer(net, input, size, act="sigmoid", bias=True, param=None, name=None):
    """
    Add a fc layer to net

    :param input: input variable name.
    :type input: str
    :param size: fully connected layer size.
    :param act: activation name
    :param param: parameter attribute, used for initialize parameters.
    :param bias: bias attribute. False will not have a bias.
    :param name: the name of fc layer. If not set, model will generate a
    readable name
    :return: output variable name.
    """
    if name is None:
        name = 'fc_%d' % uniq_id()
    if not isinstance(name, str):
        raise ValueError("name should be string")

    input_dims = scope.find_var(input).get_tensor().get_dims()

    w_name = param or name + ".w"
    init_param(param_name=w_name, dims=[input_dims[1], size])
    sgd_optimizer(net=optimize_net, param_name=w_name, learning_rate=0.01)

    pre_activation = name + ".mul.out"
    scope.new_var(pre_activation)
    mul_op = Operator("mul", X=input, Y=w_name, Out=pre_activation)
    net.add_op(mul_op)

    # create bias variable if needed
    if bias:
        bias_name = name + ".b"
        init_param(param_name=bias_name, dims=[size])
        sgd_optimizer(
            net=optimize_net, param_name=bias_name, learning_rate=0.01)
        bias_out = name + ".rowwise_add.out"
        scope.new_var(bias_out)
        rowwise_add_op = Operator(
            "rowwise_add", X=pre_activation, b=bias_name, Out=bias_out)
        net.add_op(rowwise_add_op)
        pre_activation = bias_out

    activation_op = Operator(act, X=pre_activation, Y=name)
    net.add_op(activation_op)
    scope.new_var(name)
    net.infer_shape(scope)
    return name


def cross_entropy_layer(net, input, label):
    cost_name = 'cross_entropy_%d' % uniq_id()
    cross_entropy_op = Operator(
        "onehot_cross_entropy", X=input, label=label, Y=cost_name)
    net.add_op(cross_entropy_op)
    scope.new_var(cost_name)
    net.infer_shape(scope)
    return cost_name


images = data_layer(name='pixel', dims=[BATCH_SIZE, 784])
label = data_layer(name='label', dims=[BATCH_SIZE])
fc = fc_layer(net=forward_network, input=images, size=10, act="softmax")
cost = cross_entropy_layer(net=forward_network, input=fc, label=label)
forward_network.complete_add_op(True)
print(forward_network)
backward_net = core.Operator.backward(forward_network, set())

print(backward_net)

PASS_NUM = 10
for pass_id in range(PASS_NUM):
    print pass_id