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])