def load_mnist():
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train = x_train.astype(float) / 255.
x_test = x_test.astype(float) / 255.
fields = x_train, np.squeeze(y_train)
fields_test = x_test, np.squeeze(y_test)
return fields, fields_test
def get_mnist(): """Returns the train and test splits of the MNIST digits dataset. x_train and x_test are shaped into the tensorflow image data shape and normalized to fit in the range [0, 1] """ (x_train, y_train), (x_test, y_test) = mnist.load_data() # reshape and standardize x arrays x_train = np.expand_dims(x_train, -1) / 255. x_test = np.expand_dims(x_test, -1) / 255. return x_train, x_test, y_train, y_test
import tensorflow as tf
from tensorflow.compat.v1.keras.datasets.mnist import load_data
import numpy as np
# 导入MNIST数据集
# mnist = tf.keras.datasets.mnist
# (x_train, y_train), (x_test, y_test) = mnist.load_data()
(x_train,
y_train), (x_test,
y_test) = load_data(r'D:\PythonWorkspace\TFTryout\mnist.npz')
# 转换为小数
# x_train, x_test = x_train / 255.0, x_test / 255.0
x_train = x_train / 255.0
x_test = x_test / 255.0
x_train_pro = np.reshape(x_train, (x_train.shape[0], -1))
x_test_pro = np.reshape(x_test, (x_test.shape[0], -1))
# 使用keras Sequential模型
model = tf.keras.models.Sequential([
# tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
# tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation='softmax')
])
sgd = tf.keras.optimizers.SGD(learning_rate=0.1, momentum=0.0, nesterov=False)
model.compile(optimizer=sgd,
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])