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import tensorflow as tf from tensorflow import keras from tensorflow.keras import datasets import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # x: [60k, 28, 28], # y: [60k] (x, y), _ = datasets.mnist.load_data() # 读取数据 # x: [0~255] => [0~1.] x = tf.convert_to_tensor(x, dtype=tf.float32) / 255. y = tf.convert_to_tensor(y, dtype=tf.int32) print(x.shape, y.shape, x.dtype, y.dtype) print(tf.reduce_min(x), tf.reduce_max(x)) print(tf.reduce_min(y), tf.reduce_max(y)) train_db = tf.data.Dataset.from_tensor_slices((x, y)).batch(128) # 切分成batch 相当于每128张图片是一个batch train_iter = iter(train_db) sample = next(train_iter) print('batch:', sample[0].shape, sample[1].shape) # [b, 784] => [b, 256] => [b, 128] => [b, 10] # [dim_in, dim_out], [dim_out] w1 = tf.Variable(tf.random.truncated_normal([784, 256], stddev=0.1)) b1 = tf.Variable(tf.zeros([256])) w2 = tf.Variable(tf.random.truncated_normal([256, 128], stddev=0.1)) b2 = tf.Variable(tf.zeros([128])) w3 = tf.Variable(tf.random.truncated_normal([128, 10], stddev=0.1)) b3 = tf.Variable(tf.zeros([10])) lr = 1e-3 for epoch in range(10): # iterate db for 10 对整个数据集迭代10次 for step, (x, y) in enumerate(train_db): # for every batch # 对每个batch迭代 # x:[128, 28, 28] # y: [128] # [b, 28, 28] => [b, 28*28] x = tf.reshape(x, [-1, 28*28]) with tf.GradientTape() as tape: # tf.Variable # x: [b, 28*28] # h1 = x@w1 + b1 # [b, 784]@[784, 256] + [256] => [b, 256] + [256] => [b, 256] + [b, 256] h1 = x@w1 + tf.broadcast_to(b1, [x.shape[0], 256]) h1 = tf.nn.relu(h1) # [b, 256] => [b, 128] h2 = h1@w2 + b2 h2 = tf.nn.relu(h2) # [b, 128] => [b, 10] out = h2@w3 + b3 # compute loss # out: [b, 10] # y: [b] => [b, 10] y_onehot = tf.one_hot(y, depth=10) # mse = mean(sum(y-out)^2) # [b, 10] loss = tf.square(y_onehot - out) # mean: scalar loss = tf.reduce_mean(loss) # compute gradients grads = tape.gradient(loss, [w1, b1, w2, b2, w3, b3]) # print(grads) # w1 = w1 - lr * w1_grad w1.assign_sub(lr * grads[0]) b1.assign_sub(lr * grads[1]) w2.assign_sub(lr * grads[2]) b2.assign_sub(lr * grads[3]) w3.assign_sub(lr * grads[4]) b3.assign_sub(lr * grads[5]) if step % 100 == 0: print(epoch, step, 'loss:', float(loss)) |
