import tensorflow as tf from tensorflow import keras from keras import Sequential,datasets, layers, optimizers, metrics def preprocess(x, y): """数据处理函数""" x = tf.cast(x, dtype=tf.float32) / 255. y = tf.cast(y, dtype=tf.int32) return x, y # 加载数据 (x, y), (x_test, y_test) = datasets.fashion_mnist.load_data() print(x.shape, y.shape) # 处理train数据 batch_size = 128 db = tf.data.Dataset.from_tensor_slices((x, y)) db = db.map(preprocess).shuffle(10000).batch(batch_size) # 处理test数据 db_test = tf.data.Dataset.from_tensor_slices((x_test, y_test)) db_test = db_test.map(preprocess).batch(batch_size) # # 生成train数据的迭代器 db_iter = iter(db) sample = next(db_iter) print(f'batch: {sample[0].shape,sample[1].shape}') # 设计网络结构 model = Sequential([ layers.Dense(256, activation=tf.nn.relu), # [b,784] --> [b,256] layers.Dense(128, activation=tf.nn.relu), # [b,256] --> [b,128] layers.Dense(64, activation=tf.nn.relu), # [b,128] --> [b,64] layers.Dense(32, activation=tf.nn.relu), # [b,64] --> [b,32] layers.Dense(10) # [b,32] --> [b,10], 330=32*10+10 ]) model.build(input_shape=[None, 28 * 28]) model.summary() # 调试 # w = w - lr*grad optimizer = optimizers.Adam(lr=1e-3) # 优化器,加快训练速度 def main(): """主运行函数""" for epoch in range(10): for step, (x, y) in enumerate(db): # x:[b,28,28] --> [b,784] # y:[b] x = tf.reshape(x, [-1, 28 * 28]) with tf.GradientTape() as tape: # [b,784] --> [b,10] logits = model(x) y_onehot = tf.one_hot(y, depth=10) # [b] loss_mse = tf.reduce_mean(tf.losses.MSE(y_onehot, logits)) loss_ce = tf.reduce_mean(tf.losses.categorical_crossentropy(y_onehot,logits,from_logits=True)) grads = tape.gradient(loss_ce, model.trainable_variables) optimizer.apply_gradients(zip(grads, model.trainable_variables)) if step % 100 == 0: print(epoch, step, f'loss: {float(loss_ce),float(loss_mse)}') # test total_correct = 0 total_num = 0 for x, y in db_test: # x:[b,28,28] --> [b,784] # y:[b] x = tf.reshape(x, [-1, 28 * 28]) # [b,10] logits = model(x) # logits --> prob [b,10] prob = tf.nn.softmax(logits, axis=1) # [b,10] --> [b], int32 pred = tf.argmax(prob, axis=1) pred = tf.cast(pred, dtype=tf.int32) # pred:[b] # y:[b] # correct: [b], True: equal; False: not equal correct = tf.equal(pred, y) correct = tf.reduce_sum(tf.cast(correct, dtype=tf.int32)) total_correct += int(correct) total_num += x.shape[0] acc = total_correct / total_num print(epoch, f'test acc: {acc}') if __name__ == '__main__': main()
吴裕雄--天生自然TensorFlow2教程:手写数字问题实战
猜你喜欢
转载自www.cnblogs.com/tszr/p/12228408.html
今日推荐
周排行