import tensorflow as tf import matplotlib.pyplot as plt import numpy as np datapath = r'D:datamlmnist.npz' (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data(datapath) x_train = tf.keras.utils.normalize(x_train, axis=1) x_test = tf.keras.utils.normalize(x_test, axis=1) model = tf.keras.models.Sequential() model.add(tf.keras.layers.Flatten()) model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu)) model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu)) model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax)) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(x_train, y_train, epochs=3) val_loss, val_acc = model.evaluate(x_test, y_test) print(val_loss) print(val_acc) i = 103 plt.imshow(x_test[i],cmap=plt.cm.binary) plt.show() predictions = model.predict(x_test) print(np.argmax(predictions[i]))
其中mnist.npz文件可以从google下载
https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz