init mnist

python/paddle/v2/framework/tests/CMakeLists.txt

@@ -27,3 +27,4 @@ py_test(test_uniform_random_op SRCS test_uniform_random_op.py)
 py_test(test_recurrent_op SRCS test_recurrent_op.py)
 py_test(test_sgd_op SRCS test_sgd_op.py)
 py_test(test_gradient_checker SRCS test_gradient_checker.py)
+py_test(mnist SRCS mnist.py)

python/paddle/v2/framework/tests/mnist.py

@@ -0,0 +1,208 @@
+import paddle.v2.framework.core as core
+from paddle.v2.framework.op import Operator
+import numpy
+import paddle.v2 as paddle
+
+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.ndarray)
+    tensor = scope.find_var(name).get_tensor()
+    tensor.set_dims(data.shape)
+    if data.dtype == numpy.dtype('int32'):
+        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)
+
+
+def grad_var_name(var_name):
+    return var_name + "@GRAD"
+
+
+def sgd_optimizer(net, param_name, learning_rate=0.001):
+    grad_name = grad_var_name(param_name)
+    optimize_op = Operator(
+        "sgd",
+        param=param_name,
+        grad=grad_name,
+        param_out=param_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):
+    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="softmax", 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
+
+
+def get_backward_net(forward_net):
+    net = core.Operator.backward(forward_net, set())
+    for input in net.inputs()["all"]:
+        var = scope.new_var(input)
+        var.get_tensor()
+    for output in net.outputs()["all"]:
+        var = scope.new_var(output)
+        var.get_tensor()
+    return net
+
+
+def print_inputs_outputs(op):
+    print("===============" + op.type() + "==============")
+    print("***inputs:***")
+    for input in op.inputs()["all"]:
+        print input, scope.find_var(input).get_tensor().get_dims()
+    print("***outputs:***")
+    for output in op.outputs()["all"]:
+        print output, scope.find_var(output).get_tensor().get_dims()
+    print("")
+    print("")
+
+
+def set_cost():
+    cost_data = numpy.array(scope.find_var("cross_entropy_1").get_tensor())
+    # print(cost_data)
+    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)
+    cost_grad.set(cost_data, place)
+
+
+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)
+backward_net = get_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)
+
+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):
+    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)
+    forward_network.run(scope, dev_ctx)
+    set_cost()
+    backward_net.infer_shape(scope)
+    backward_net.run(scope, dev_ctx)
+
+    optimize_net.run(scope, dev_ctx)