# -*- encoding: utf-8 -*-    
"""
@Author     :   zYx.Tom
@Contact    :   526614962@qq.com
@site       :   https://zhuyuanxiang.github.io
---------------------------
@Software   :   PyCharm
@Project    :   tensorflow_cookbook
@File       :   C1001_unit_tests.py
@Version    :   v0.1
@Time       :   2019-12-09 17:07
@License    :   (C)Copyright 2018-2019, zYx.Tom
@Reference  :   《TensorFlow机器学习实战指南，Nick McClure》, Sec10，P243
@Desc       :   TensorFlow 产品化，使用 TensorFlow 实现 单元测试
@理解：了解 TensorFlow 如何测试，但是没有给出很好的测试案例。
"""
# common imports
import os
import sys

import matplotlib.pyplot as plt
import numpy as np  # pip install numpy<1.17，小于1.17就不会报错
import sklearn
import tensorflow as tf
import winsound
from tensorflow.python.framework import ops

# 设置数据显示的精确度为小数点后3位
np.set_printoptions(precision = 8, suppress = True, threshold = np.inf, linewidth = 200)
# 利用随机种子，保证随机数据的稳定性，使得每次随机测试的结果一样
seed = 42
np.random.seed(seed)
tf.set_random_seed(seed)
# Python ≥3.5 is required
assert sys.version_info >= (3, 5)
# Scikit-Learn ≥0.20 is required
assert sklearn.__version__ >= "0.20"
# numpy 1.16.4 is required
assert np.__version__ in ["1.16.5", "1.16.4"]
# 屏蔽警告：Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
# 初始化默认的计算图
ops.reset_default_graph()
# Open graph session
sess = tf.Session()
# ----------------------------------------------------------------------
# Load data
mnist = tf.keras.datasets.mnist
(train_xdata, train_labels), (test_xdata, test_labels) = mnist.load_data()
train_xdata = train_xdata / 255.0
test_xdata = test_xdata / 255.0

# Set model parameters
batch_size = 100
learning_rate = 0.005
evaluation_size = 100
image_width = train_xdata[0].shape[0]
image_height = train_xdata[0].shape[1]
target_size = max(train_labels) + 1
num_channels = 1  # greyscale = 1 channel
generations = 100
eval_every = 5
conv1_features = 25
conv2_features = 50
max_pool_size1 = 2  # NxN window for 1st max pool layer
max_pool_size2 = 2  # NxN window for 2nd max pool layer
fully_connected_size1 = 100
dropout_prob = 0.75

# Declare model placeholders
x_input_shape = (batch_size, image_width, image_height, num_channels)
x_input = tf.placeholder(tf.float32, shape = x_input_shape)
y_target = tf.placeholder(tf.int32, shape = (batch_size))
eval_input_shape = (evaluation_size, image_width, image_height, num_channels)
eval_input = tf.placeholder(tf.float32, shape = eval_input_shape)
eval_target = tf.placeholder(tf.int32, shape = (evaluation_size))

# Dropout placeholder
dropout = tf.placeholder(tf.float32, shape = ())

# Declare model parameters
conv1_weight = tf.Variable(tf.truncated_normal([4, 4, num_channels, conv1_features],
                                               stddev = 0.1, dtype = tf.float32))
conv1_bias = tf.Variable(tf.zeros([conv1_features], dtype = tf.float32))

conv2_weight = tf.Variable(tf.truncated_normal([4, 4, conv1_features, conv2_features],
                                               stddev = 0.1, dtype = tf.float32))
conv2_bias = tf.Variable(tf.zeros([conv2_features], dtype = tf.float32))

# fully connected variables
resulting_width = image_width // (max_pool_size1 * max_pool_size2)
resulting_height = image_height // (max_pool_size1 * max_pool_size2)
full1_input_size = resulting_width * resulting_height * conv2_features
full1_weight = tf.Variable(
        tf.truncated_normal([full1_input_size, fully_connected_size1], stddev = 0.1, dtype = tf.float32))
full1_bias = tf.Variable(tf.truncated_normal([fully_connected_size1], stddev = 0.1, dtype = tf.float32))
full2_weight = tf.Variable(tf.truncated_normal([fully_connected_size1, target_size],
                                               stddev = 0.1, dtype = tf.float32))
full2_bias = tf.Variable(tf.truncated_normal([target_size], stddev = 0.1, dtype = tf.float32))


# Initialize Model Operations
def my_conv_net(input_data):
    # First Conv-ReLU-MaxPool Layer
    conv1 = tf.nn.conv2d(input_data, conv1_weight, strides = [1, 1, 1, 1], padding = 'SAME')
    relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_bias))
    max_pool1 = tf.nn.max_pool(relu1, ksize = [1, max_pool_size1, max_pool_size1, 1],
                               strides = [1, max_pool_size1, max_pool_size1, 1], padding = 'SAME')

    # Second Conv-ReLU-MaxPool Layer
    conv2 = tf.nn.conv2d(max_pool1, conv2_weight, strides = [1, 1, 1, 1], padding = 'SAME')
    relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_bias))
    max_pool2 = tf.nn.max_pool(relu2, ksize = [1, max_pool_size2, max_pool_size2, 1],
                               strides = [1, max_pool_size2, max_pool_size2, 1], padding = 'SAME')

    # Transform Output into a 1xN layer for next fully connected layer
    final_conv_shape = max_pool2.get_shape().as_list()
    final_shape = final_conv_shape[1] * final_conv_shape[2] * final_conv_shape[3]
    flat_output = tf.reshape(max_pool2, [final_conv_shape[0], final_shape])

    # First Fully Connected Layer
    fully_connected1 = tf.nn.relu(tf.add(tf.matmul(flat_output, full1_weight), full1_bias))

    # Second Fully Connected Layer
    final_model_output = tf.add(tf.matmul(fully_connected1, full2_weight), full2_bias)

    # Add dropout
    final_model_output = tf.nn.dropout(final_model_output, dropout)

    return final_model_output


model_output = my_conv_net(x_input)
test_model_output = my_conv_net(eval_input)

# Declare Loss Function (softmax cross entropy)
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits = model_output, labels = y_target))

# Create a prediction function
prediction = tf.nn.softmax(model_output)
test_prediction = tf.nn.softmax(test_model_output)


# Create accuracy function
def get_accuracy(logits, targets):
    batch_predictions = np.argmax(logits, axis = 1)
    num_correct = np.sum(np.equal(batch_predictions, targets))
    return 100. * num_correct / batch_predictions.shape[0]


# Create an optimizer
my_optimizer = tf.train.MomentumOptimizer(learning_rate, 0.9)
train_step = my_optimizer.minimize(loss)

# Initialize Variables
init = tf.global_variables_initializer()
sess.run(init)


# Check values of tensors!
class drop_out_test(tf.test.TestCase):
    # Make sure that we don't drop too much
    def dropout_greaterthan(self):
        with self.test_session():
            self.assertGreater(dropout.eval(), 0.25)


# Test accuracy function
class accuracy_test(tf.test.TestCase):
    # Make sure accuracy function behaves correctly
    def accuracy_exact_test(self):
        with self.test_session():
            test_preds = [[0.9, 0.1], [0.01, 0.99]]
            test_targets = [0, 1]
            test_acc = get_accuracy(test_preds, test_targets)
            self.assertEqual(test_acc.eval(), 100.)


# Test tensorshape
class shape_test(tf.test.TestCase):
    # Make sure our model output is size [batch_size, num_classes]
    def output_shape_test(self):
        with self.test_session():
            numpy_array = np.ones([batch_size, target_size])
            self.assertShapeEqual(numpy_array, model_output)


def main(argv):
    # Start training loop
    train_loss = []
    train_acc = []
    test_acc = []
    for i in range(generations):
        rand_index = np.random.choice(len(train_xdata), size = batch_size)
        rand_x = train_xdata[rand_index]
        rand_x = np.expand_dims(rand_x, 3)
        rand_y = train_labels[rand_index]
        train_dict = {x_input: rand_x, y_target: rand_y, dropout: dropout_prob}

        sess.run(train_step, feed_dict = train_dict)
        temp_train_loss, temp_train_preds = sess.run([loss, prediction], feed_dict = train_dict)
        temp_train_acc = get_accuracy(temp_train_preds, rand_y)

        if (i + 1) % eval_every == 0:
            eval_index = np.random.choice(len(test_xdata), size = evaluation_size)
            eval_x = test_xdata[eval_index]
            eval_x = np.expand_dims(eval_x, 3)
            eval_y = test_labels[eval_index]
            test_dict = {eval_input: eval_x, eval_target: eval_y, dropout: 1.0}
            test_preds = sess.run(test_prediction, feed_dict = test_dict)
            temp_test_acc = get_accuracy(test_preds, eval_y)

            # Record and print results
            train_loss.append(temp_train_loss)
            train_acc.append(temp_train_acc)
            test_acc.append(temp_test_acc)
            acc_and_loss = [(i + 1), temp_train_loss, temp_train_acc, temp_test_acc]
            acc_and_loss = [np.round(x, 2) for x in acc_and_loss]
            print('Generation # {}. Train Loss: {:.2f}. Train Acc (Test Acc): {:.2f} ({:.2f})'.format(*acc_and_loss))


if __name__ == '__main__':
    print("sys.argv =", sys.argv)
    cmd_args = sys.argv
    if len(cmd_args) > 1 and cmd_args[1] == 'test':
        # Perform unit tests
        tf.test.main(argv = cmd_args[1:])
    else:
        # Run TF App
        tf.app.run(main = None, argv = cmd_args)
    # ----------------------------------------------------------------------
    # 运行结束的提醒
    winsound.Beep(600, 500)
    if len(plt.get_fignums()) != 0:
        plt.show()
    pass
