import tensorflow as tf

import tensorflow_datasets as tfds

import numpy as np

import matplotlib.pyplot as plt

import math

import tqdm

import tqdm.auto

tqdm.tqdm = tqdm.auto.tqdm

print(tf.__version__)

#导入库

dataset,metadata = tfds.load('fashion_mnist',as_supervised=True,with_info=True)

train_dataset,test_dataset = dataset['train'],dataset['test']

#导入数据集

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',

               'Sandal',      'Shirt',   'Sneaker',  'Bag',   'Ankle boot']

#映射标签

num_train_examples = metadata.splits['train'].num_examples

num_test_examples = metadata.splits['test'].num_examples

print("训练样本个数: {}".format(num_train_examples))

print("测试样本个数:{}".format(num_test_examples))

def normalize(images,labels): #定义标准化函数

  images = tf.cast(images,tf.float32)

  images /= 255

  return images,labels

train_dataset = train_dataset.map(normalize)#标准化

test_dataset = test_dataset.map(normalize) #标准化

#绘制一个图像

for image, label in test_dataset.take(1):

  break

image = image.numpy().reshape((28,28))

plt.figure()

plt.imshow(image, cmap=plt.cm.binary)

plt.colorbar()

plt.grid(False)

plt.show()

#显示前25幅图像。训练集并在每个图像下面显示类名

plt.figure(figsize=(10,10))

i = 0

for (image, label) in test_dataset.take(25):

    image = image.numpy().reshape((28,28))

    plt.subplot(5,5,i+1)

    plt.xticks([])

    plt.yticks([])

    plt.grid(False)

    plt.imshow(image, cmap=plt.cm.binary)

    plt.xlabel(class_names[label])

    i += 1

plt.show()

#建立模型

    model = tf.keras.Sequential([

        tf.keras.layers.Flatten(input_shape=(28,28,1)), #输入层

        tf.keras.layers.Dense(256,activation=tf.nn.relu),#隐藏层1

        tf.keras.layers.Dense(128,activation=tf.nn.relu),#隐藏层2

        tf.keras.layers.Dense(10,activation=tf.nn.softmax)#输出层

    ])

#定义优化器和损失函数

model.compile(optimizer='adam',

              loss='sparse_categorical_crossentropy',

             metrics=['accuracy'])

#设置训练参数

BATCH_SIZE = 32

train_dataset = train_dataset.repeat().shuffle(num_train_examples).batch(BATCH_SIZE)

test_dataset = test_dataset.batch(BATCH_SIZE)

#训练模型

model.fit(train_dataset, epochs=5, steps_per_epoch=math.ceil(num_train_examples/BATCH_SIZE))

test_loss, test_accuracy = model.evaluate(test_dataset, steps=math.ceil(num_test_examples/32))

print('Accuracy on test dataset:', test_accuracy)

for test_images, test_labels in test_dataset.take(1):

  test_images = test_images.numpy()

  test_labels = test_labels.numpy()

  predictions = model.predict(test_images)

predictions.shape

predictions[0]

np.argmax(predictions[0])

test_labels[0]

def plot_image(i, predictions_array, true_labels, images):

  predictions_array, true_label, img = predictions_array[i], true_labels[i], images[i]

  plt.grid(False)

  plt.xticks([])

  plt.yticks([])

  plt.imshow(img[...,0], cmap=plt.cm.binary)

  predicted_label = np.argmax(predictions_array)

  if predicted_label == true_label:

    color = 'blue'

  else:

    color = 'red'

  plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],

                                100*np.max(predictions_array),

                                class_names[true_label]),

                                color=color)

def plot_value_array(i, predictions_array, true_label):

  predictions_array, true_label = predictions_array[i], true_label[i]

  plt.grid(False)

  plt.xticks([])

  plt.yticks([])

  thisplot = plt.bar(range(10), predictions_array, color="#777777")

  plt.ylim([0, 1])

  predicted_label = np.argmax(predictions_array)

  thisplot[predicted_label].set_color('red')

  thisplot[true_label].set_color('blue')

i = 0

plt.figure(figsize=(6,3))

plt.subplot(1,2,1)

plot_image(i, predictions, test_labels, test_images)

plt.subplot(1,2,2)

plot_value_array(i, predictions,  test_labels)

i = 12

plt.figure(figsize=(6,3))

plt.subplot(1,2,1)

plot_image(i, predictions, test_labels, test_images)

plt.subplot(1,2,2)

plot_value_array(i, predictions,  test_labels)

num_rows = 5

num_cols = 3

num_images = num_rows*num_cols

plt.figure(figsize=(2*2*num_cols, 2*num_rows))

for i in range(num_images):

  plt.subplot(num_rows, 2*num_cols, 2*i+1)

  plot_image(i, predictions, test_labels, test_images)

  plt.subplot(num_rows, 2*num_cols, 2*i+2)

  plot_value_array(i, predictions, test_labels)

img = test_images[0]

print(img.shape)

img = np.array([img])

print(img.shape)

predictions_single = model.predict(img)

print(predictions_single)

plot_value_array(0, predictions_single, test_labels)

_ = plt.xticks(range(10), class_names, rotation=45)

np.argmax(predictions_single[0])