日月光华TensorFlow2.0个人收集整理的数据集和代码

import tensorflow as tf import pandas as pd import matplotlib.pyplot as plt import numpy as np print('tensorflow version: {}'.format(tf.version)) (train_image, train_label), (test_image, test_label) = tf.keras.datasets.fashion_mnist.load_data() train_image.shape train_label.shape test_image.shape test_label.shape plt.imshow(train_image[0]) plt.show() train_image = train_image / 255 test_image = test_image / 255 model = tf.keras.Sequential() model.add(tf.keras.layers.Flatten(input_shape=(28, 28))) # 将输入的二维图片一维化 model.add(tf.keras.layers.Dense(128, activation='relu')) model.add(tf.keras.layers.Dense(10, activation='softmax')) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['acc'] ) model.fit(train_image, train_label, epochs=5) model.evaluate(test_image, test_label) # 独热编码与交叉熵损失函数 train_label_onehot = tf.keras.utils.to_categorical(train_label) test_label_onehot = tf.keras.utils.to_categorical(train_label) model = tf.keras.Sequential() model.add(tf.keras.layers.Flatten(input_shape=(28, 28))) # 将输入的二维图片一维化 model.add(tf.keras.layers.Dense(128, activation='relu')) model.add(tf.keras.layers.Dense(10, activation='softmax')) model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.01), loss='categorical_crossentropy', metrics=['acc'] ) # model.compile(optimizer='adam', # loss='categorical_crossentropy', # metrics=['acc'] # ) # model.fit(train_image, train_label_onehot, epochs=5) predict = model.predict(test_image) np.argmax(predict[0]) # 网络优化与超参数选择 model = tf.keras.Sequential() model.add(tf.keras.layers.Flatten(input_shape=(28, 28))) # 将输入的二维图片一维化 model.add(tf.keras.layers.Dense(128, activation='relu')) model.add(tf.keras.layers.Dense(128, activation='relu')) model.add(tf.keras.layers.Dense(128, activation='relu')) model.add(tf.keras.layers.Dense(10, activation='softmax')) model.summary() model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['acc'] ) model.fit(train_image, train_label_onehot, epochs=10)