def get_batch(self, batch_size):
    # 从数据集中随机取出batch_size个元素并返回
    index = np.random.randint(0, np.shape(self.train_data)[0], batch_size)
    return self.train_data[index, :], self.train_label[index]

#使用继承 tf.keras.Model 类建立的 Keras 模型同样可以以相同方法导出，唯须注意 call 方法需要以 @tf.function 修饰，
#以转化为 SavedModel 支持的计算图，代码如下：
@tf.function
def call(self, inputs):         # [batch_size, 28, 28, 1]
    x = self.flatten(inputs)    # [batch_size, 784]
    x = self.dense1(x)          # [batch_size, 100]
    x = self.dense2(x)          # [batch_size, 10]
    output = tf.nn.softmax(x)
    return output

    loss = tf.keras.losses.sparse_categorical_crossentropy(y_true=y, y_pred=y_pred)
    loss = tf.reduce_mean(loss)
    print("batch %d: loss %f" % (batch_index, loss.numpy()))
grads = tape.gradient(loss, model.variables)
optimizer.apply_gradients(grads_and_vars=zip(grads, model.variables))

# Save the model
model.save('path_to_my_model.h5')

# Recreate the exact same model purely from the file
new_model = keras.models.load_model('path_to_my_model.h5')

def compile(self, learning_rate, momentum):
    """Gets the model ready for training. Adds losses, regularization, and
    metrics. Then calls the Keras compile() function.
    """
    # Optimizer object
    optimizer = keras.optimizers.SGD(
        lr=learning_rate, momentum=momentum,
        clipnorm=self.config.GRADIENT_CLIP_NORM)
    # Add Losses
    # First, clear previously set losses to avoid duplication
    self.keras_model._losses = []
    self.keras_model._per_input_losses = {}
    loss_names = [
        "rpn_class_loss",  "rpn_bbox_loss",
        "mrcnn_class_loss", "mrcnn_bbox_loss", "mrcnn_mask_loss"]
    for name in loss_names:
        layer = self.keras_model.get_layer(name)
        if layer.output in self.keras_model.losses:
            continue
        loss = (
            tf.reduce_mean(layer.output, keepdims=True)
            * self.config.LOSS_WEIGHTS.get(name, 1.))
        self.keras_model.add_loss(loss)

    # Add L2 Regularization
    # Skip gamma and beta weights of batch normalization layers.
    reg_losses = [
        keras.regularizers.l2(self.config.WEIGHT_DECAY)(w) / tf.cast(tf.size(w), tf.float32)
        for w in self.keras_model.trainable_weights
        if 'gamma' not in w.name and 'beta' not in w.name]
    self.keras_model.add_loss(tf.add_n(reg_losses))

    # Compile
    self.keras_model.compile(
        optimizer=optimizer,
        loss=[None] * len(self.keras_model.outputs))

    # Add metrics for losses
    for name in loss_names:
        if name in self.keras_model.metrics_names:
            continue
        layer = self.keras_model.get_layer(name)
        self.keras_model.metrics_names.append(name)
        loss = (
            tf.reduce_mean(layer.output, keepdims=True)
            * self.config.LOSS_WEIGHTS.get(name, 1.))
        self.keras_model.metrics_tensors.append(loss)

import tensorflow as tf
import numpy as np
class DataLoader():
    def __init__(self):
        path = tf.keras.utils.get_file('nietzsche.txt',
            origin='https://s3.amazonaws.com/text-datasets/nietzsche.txt')
        with open(path, encoding='utf-8') as f:
            self.raw_text = f.read().lower()
        self.chars = sorted(list(set(self.raw_text)))
        self.char_indices = dict((c, i) for i, c in enumerate(self.chars))
        self.indices_char = dict((i, c) for i, c in enumerate(self.chars))
        self.text = [self.char_indices[c] for c in self.raw_text]

    def get_batch(self, seq_length, batch_size):
        seq = []
        next_char = []
        for i in range(batch_size):
            index = np.random.randint(0, len(self.text) - seq_length)
            seq.append(self.text[index:index+seq_length])
            next_char.append(self.text[index+seq_length])
        return np.array(seq), np.array(next_char)       # [batch_size, seq_length], [num_batch]


class RNN(tf.keras.Model):
    def __init__(self, num_chars, batch_size, seq_length):
        super().__init__()
        self.num_chars = num_chars
        self.seq_length = seq_length
        self.batch_size = batch_size
        self.cell = tf.keras.layers.LSTMCell(units=256)
        self.dense = tf.keras.layers.Dense(units=self.num_chars)

    def call(self, inputs, from_logits=False):
        inputs = tf.one_hot(inputs, depth=self.num_chars)       # [batch_size, seq_length, num_chars]
        state = self.cell.get_initial_state(batch_size=self.batch_size, dtype=tf.float32)
        for t in range(self.seq_length):
            output, state = self.cell(inputs[:, t, :], state)
        logits = self.dense(output)
        if from_logits:
            return logits
        else:
            return tf.nn.softmax(logits)
num_batches = 10
seq_length = 40
batch_size = 50
learning_rate = 1e-3
data_loader = DataLoader()
model = RNN(num_chars=len(data_loader.chars), batch_size=batch_size, seq_length=seq_length)
optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
for batch_index in range(num_batches):
    X, y = data_loader.get_batch(seq_length, batch_size)
    with tf.GradientTape() as tape:
        y_pred = model(X)
        loss = tf.keras.losses.sparse_categorical_crossentropy(y_true=y, y_pred=y_pred)
        loss = tf.reduce_mean(loss)
        print("batch %d: loss %f" % (batch_index, loss.numpy()))
    grads = tape.gradient(loss, model.variables)
    optimizer.apply_gradients(grads_and_vars=zip(grads, model.variables))

def predict(self, inputs, temperature=1.):
    batch_size, _ = tf.shape(inputs)
    logits = self(inputs, from_logits=True)
    print(logits)
    print(temperature)
    prob = tf.nn.softmax(logits / temperature).numpy()
    return np.array([np.random.choice(self.num_chars, p=prob[i, :])
                     for i in range(batch_size.numpy())])

X_, _ = data_loader.get_batch(seq_length, 1)

for diversity in [0.2, 0.5, 1.0, 1.2]:
    X = X_
    print("diversity %f:" % diversity)
    for t in range(400):
        y_pred = model.predict(X, diversity)
        print(data_loader.indices_char[y_pred[0]], end='', flush=True)
        X = np.concatenate([X[:, 1:], np.expand_dims(y_pred, axis=1)], axis=-1)
    print("\n")

runfile('F:/pyth/pj3/study3.py', wdir='F:/pyth/pj3')
batch 0: loss 4.044459
batch 1: loss 4.025946
batch 2: loss 4.001545
batch 3: loss 3.980800
batch 4: loss 3.945248
batch 5: loss 3.867068
batch 6: loss 3.684950
batch 7: loss 3.236459
batch 8: loss 3.574704
batch 9: loss 3.551273
diversity 0.200000:
Traceback (most recent call last):
  File "<input>", line 1, in <module>
  File "D:\Program Files\JetBrains\PyCharm 2019.2.3\helpers\pydev\_pydev_bundle\pydev_umd.py", line 197, in runfile
    pydev_imports.execfile(filename, global_vars, local_vars)  # execute the script
  File "D:\Program Files\JetBrains\PyCharm 2019.2.3\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
    exec(compile(contents+"\n", file, 'exec'), glob, loc)
  File "F:/pyth/pj3/study3.py", line 93, in <module>
    y_pred = model.predict(X, diversity)
  File "D:\ProgramData\Anaconda3\envs\kingtf2\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 909, in predict
    use_multiprocessing=use_multiprocessing)
  File "D:\ProgramData\Anaconda3\envs\kingtf2\lib\site-packages\tensorflow_core\python\keras\engine\training_arrays.py", line 722, in predict
    callbacks=callbacks)
  File "D:\ProgramData\Anaconda3\envs\kingtf2\lib\site-packages\tensorflow_core\python\keras\engine\training_arrays.py", line 362, in model_iteration
    batch_ids = index_array[batch_start:batch_end]
TypeError: slice indices must be integers or None or have an __index__ method
WARNING:tensorflow:Tensor._shape is private, use Tensor.shape instead. Tensor._shape will eventually be removed.
WARNING:tensorflow:Tensor._shape is private, use Tensor.shape instead. Tensor._shape will eventually be removed.

gpus = tf.config.experimental.list_physical_devices(device_type='GPU')
for gpu in gpus:
    tf.config.experimental.set_memory_growth(device=gpu, True)

tf.config.experimental.set_memory_growth(gpu, True)
或
tf.config.experimental.set_memory_growth(device=gpu, enable=True)