# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import paddle.fluid as fluid
import numpy as np


def simple_fc_net(use_feed=None):
    img = fluid.layers.data(name='image', shape=[784], dtype='float32')
    label = fluid.layers.data(name='label', shape=[1], dtype='int64')

    hidden = img
    for _ in range(4):
        hidden = fluid.layers.fc(
            hidden,
            size=200,
            act='relu',
            bias_attr=fluid.ParamAttr(
                initializer=fluid.initializer.Constant(value=1.0)))
    prediction = fluid.layers.fc(hidden, size=10, act='softmax')
    loss = fluid.layers.cross_entropy(input=prediction, label=label)
    loss = fluid.layers.mean(loss)
    return loss


def fc_with_batchnorm(use_feed=None):
    img = fluid.layers.data(name='image', shape=[784], dtype='float32')
    label = fluid.layers.data(name='label', shape=[1], dtype='int64')

    hidden = img
    for _ in range(2):
        hidden = fluid.layers.fc(
            hidden,
            size=200,
            act='relu',
            bias_attr=fluid.ParamAttr(
                initializer=fluid.initializer.Constant(value=1.0)))

        hidden = fluid.layers.batch_norm(input=hidden)

    prediction = fluid.layers.fc(hidden, size=10, act='softmax')
    loss = fluid.layers.cross_entropy(input=prediction, label=label)
    loss = fluid.layers.mean(loss)
    return loss


def init_data(batch_size=32, img_shape=[784], label_range=9):
    np.random.seed(5)
    assert isinstance(img_shape, list)
    input_shape = [batch_size] + img_shape
    img = np.random.random(size=input_shape).astype(np.float32)
    label = np.array(
        [np.random.randint(0, label_range) for _ in range(batch_size)]).reshape(
            (-1, 1)).astype("int64")
    return img, label