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ch03/demo.py

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+import  tensorflow as tf
+from    tensorflow.keras import datasets, layers, optimizers, Sequential, metrics
+
+
+# 设置GPU使用方式
+# 获取GPU列表
+gpus = tf.config.experimental.list_physical_devices('GPU')
+if gpus:
+  try:
+    # 设置GPU为增长式占用
+    for gpu in gpus:
+      tf.config.experimental.set_memory_growth(gpu, True) 
+  except RuntimeError as e:
+    # 打印异常
+    print(e)
+
+(xs, ys),_ = datasets.mnist.load_data()
+print('datasets:', xs.shape, ys.shape, xs.min(), xs.max())
+
+batch_size = 32
+
+xs = tf.convert_to_tensor(xs, dtype=tf.float32) / 255.
+db = tf.data.Dataset.from_tensor_slices((xs,ys))
+db = db.batch(batch_size).repeat(30)
+
+
+model = Sequential([layers.Dense(256, activation='relu'), 
+                     layers.Dense(128, activation='relu'),
+                     layers.Dense(10)])
+model.build(input_shape=(4, 28*28))
+model.summary()
+
+optimizer = optimizers.SGD(lr=0.01)
+acc_meter = metrics.Accuracy()
+
+for step, (x,y) in enumerate(db):
+
+    with tf.GradientTape() as tape:
+        # 打平操作，[b, 28, 28] => [b, 784]
+        x = tf.reshape(x, (-1, 28*28))
+        # Step1. 得到模型输出output [b, 784] => [b, 10]
+        out = model(x)
+        # [b] => [b, 10]
+        y_onehot = tf.one_hot(y, depth=10)
+        # 计算差的平方和，[b, 10]
+        loss = tf.square(out-y_onehot)
+        # 计算每个样本的平均误差，[b]
+        loss = tf.reduce_sum(loss) / x.shape[0]
+
+
+    acc_meter.update_state(tf.argmax(out, axis=1), y)
+
+    grads = tape.gradient(loss, model.trainable_variables)
+    optimizer.apply_gradients(zip(grads, model.trainable_variables))
+
+
+    if step % 200==0:
+
+        print(step, 'loss:', float(loss), 'acc:', acc_meter.result().numpy())
+        acc_meter.reset_states()