为什么class OneJoint(layers.Layer): def __init__(self, units=1, input_dim=3): super(OneJoint, self).__init__() w_init = tf.random_normal_initializer() self.w = tf.Variable( initial_value=w_init(shape=(input_dim, units), dtype="float32"), trainable=True, name='融合' ) def call(self, inputs, *args, **kwargs): return tf.matmul(inputs, self.w)在最小化损失时，变量不存在梯度

这个问题属于技术问题，我可以回答。这个问题可能是由于变量没有被正确地定义或初始化导致的。在最小化损失时，如果变量不存在梯度，可能会导致模型无法更新变量，从而无法正确地训练模型。建议检查变量的定义和初始化是否正确，并确保在训练过程中正确地计算梯度。

相关问题

定义函数用tf.keras.layers.Layer

tf.keras.layers.Layer是一个基类，用于构建自定义层的模板。可以通过继承该类来创建自己的层。以下是一个使用tf.keras.layers.Layer定义函数的示例：

import tensorflow as tf

class MyLayer(tf.keras.layers.Layer):
    def __init__(self, units=32, input_dim=32):
        super(MyLayer, self).__init__()
        self.w = self.add_weight(shape=(input_dim, units),
                                 initializer='random_normal',
                                 trainable=True)
        self.b = self.add_weight(shape=(units,),
                                 initializer='random_normal',
                                 trainable=True)

    def call(self, inputs):
        return tf.matmul(inputs, self.w) + self.b

上述代码定义了一个名为MyLayer的自定义层，该层具有可训练的权重和偏置，输入维度为input_dim，输出维度为units。在call方法中，该层将输入与权重矩阵相乘并添加偏置，然后返回结果。

可以使用该层作为模型的一部分，例如：

model = tf.keras.Sequential([
    MyLayer(64, input_shape=(784,)),
    tf.keras.layers.Activation('relu'),
    tf.keras.layers.Dense(10),
    tf.keras.layers.Activation('softmax')
])

解决AttributeError: module 'tensorflow._api.v2.compat.v1.nn.rnn_cell' has no attribute 'seq2seq'

在 TensorFlow 2.0 中，`tf.nn.rnn_cell` 模块已经被移除了，因此 `tf.nn.rnn_cell.seq2seq` 也不再可用。相反，您可以使用 `tf.keras.layers` 中的相应函数来构建您的模型。下面是一个使用 `tf.keras` 实现 seq2seq 模型的示例：

import tensorflow as tf

# 定义编码器
class Encoder(tf.keras.Model):
    def __init__(self, vocab_size, embedding_dim, enc_units, batch_sz):
        super(Encoder, self).__init__()
        self.batch_sz = batch_sz
        self.enc_units = enc_units
        self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
        self.gru = tf.keras.layers.GRU(self.enc_units, return_sequences=True, return_state=True, recurrent_initializer='glorot_uniform')

    def call(self, x, hidden):
        x = self.embedding(x)
        output, state = self.gru(x, initial_state = hidden)
        return output, state

    def initialize_hidden_state(self):
        return tf.zeros((self.batch_sz, self.enc_units))

# 定义注意力层
class BahdanauAttention(tf.keras.layers.Layer):
    def __init__(self, units):
        super(BahdanauAttention, self).__init__()
        self.W1 = tf.keras.layers.Dense(units)
        self.W2 = tf.keras.layers.Dense(units)
        self.V = tf.keras.layers.Dense(1)

    def call(self, query, values):
        # query: 上一时间步的隐藏状态，shape=(batch_size, hidden_size)
        # values: 编码器的输出，shape=(batch_size, max_length, hidden_size)
        hidden_with_time_axis = tf.expand_dims(query, 1)

        score = self.V(tf.nn.tanh(
            self.W1(values) + self.W2(hidden_with_time_axis)))

        # attention_weights shape == (batch_size, max_length, 1)
        attention_weights = tf.nn.softmax(score, axis=1)

        # context_vector shape after sum == (batch_size, hidden_size)
        context_vector = attention_weights * values
        context_vector = tf.reduce_sum(context_vector, axis=1)

        return context_vector, attention_weights

# 定义解码器
class Decoder(tf.keras.Model):
    def __init__(self, vocab_size, embedding_dim, dec_units, batch_sz):
        super(Decoder, self).__init__()
        self.batch_sz = batch_sz
        self.dec_units = dec_units
        self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
        self.gru = tf.keras.layers.GRU(self.dec_units, return_sequences=True, return_state=True, recurrent_initializer='glorot_uniform')
        self.fc = tf.keras.layers.Dense(vocab_size)

        # 用于注意力
        self.attention = BahdanauAttention(self.dec_units)

    def call(self, x, hidden, enc_output):
        # enc_output shape == (batch_size, max_length, hidden_size)
        context_vector, attention_weights = self.attention(hidden, enc_output)

        # x shape after passing through embedding == (batch_size, 1, embedding_dim)
        x = self.embedding(x)

        # 将上一时间步的隐藏状态和注意力向量拼接起来作为输入传给 GRU
        x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)

        # 将拼接后的向量传给 GRU
        output, state = self.gru(x)

        # output shape == (batch_size * 1, hidden_size)
        output = tf.reshape(output, (-1, output.shape[2]))

        # output shape == (batch_size, vocab)
        x = self.fc(output)

        return x, state, attention_weights

# 定义损失函数和优化器
optimizer = tf.keras.optimizers.Adam()
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction='none')

def loss_function(real, pred):
    mask = tf.math.logical_not(tf.math.equal(real, 0))
    loss_ = loss_object(real, pred)

    mask = tf.cast(mask, dtype=loss_.dtype)
    loss_ *= mask

    return tf.reduce_mean(loss_)

# 定义训练步骤
@tf.function
def train_step(inp, targ, enc_hidden):
    loss = 0

    with tf.GradientTape() as tape:
        enc_output, enc_hidden = encoder(inp, enc_hidden)

        dec_hidden = enc_hidden

        dec_input = tf.expand_dims([tokenizer.word_index['<start>']] * BATCH_SIZE, 1)

        # teacher forcing - 将目标词作为下一个输入传给解码器
        for t in range(1, targ.shape[1]):
            # 将编码器的输出和上一时间步的隐藏状态传给解码器
            predictions, dec_hidden, _ = decoder(dec_input, dec_hidden, enc_output)

            loss += loss_function(targ[:, t], predictions)

            # 使用 teacher forcing
            dec_input = tf.expand_dims(targ[:, t], 1)

    batch_loss = (loss / int(targ.shape[1]))

    variables = encoder.trainable_variables + decoder.trainable_variables

    gradients = tape.gradient(loss, variables)

    optimizer.apply_gradients(zip(gradients, variables))

    return batch_loss

# 定义预测函数
def evaluate(sentence):
    attention_plot = np.zeros((max_length_targ, max_length_inp))

    sentence = preprocess_sentence(sentence)

    inputs = [tokenizer.word_index[i] for i in sentence.split(' ')]
    inputs = tf.keras.preprocessing.sequence.pad_sequences([inputs], maxlen=max_length_inp, padding='post')
    inputs = tf.convert_to_tensor(inputs)

    result = ''

    hidden = [tf.zeros((1, units))]
    enc_out, enc_hidden = encoder(inputs, hidden)

    dec_hidden = enc_hidden
    dec_input = tf.expand_dims([tokenizer.word_index['<start>']], 0)

    for t in range(max_length_targ):
        predictions, dec_hidden, attention_weights = decoder(dec_input, dec_hidden, enc_out)

        # 存储注意力权重以便后面制图
        attention_weights = tf.reshape(attention_weights, (-1, ))
        attention_plot[t] = attention_weights.numpy()

        predicted_id = tf.argmax(predictions[0]).numpy()

        result += tokenizer.index_word[predicted_id] + ' '

        if tokenizer.index_word[predicted_id] == '<end>':
            return result, sentence, attention_plot

        # 将预测的 ID 作为下一个解码器输入的 ID
        dec_input = tf.expand_dims([predicted_id], 0)

    return result, sentence, attention_plot

在上面的代码中，我们使用了 `tf.keras.layers` 中的 `Embedding`、`GRU` 和 `Dense` 层来构建编码器和解码器，使用 `tf.keras.optimizers.Adam` 作为优化器，使用 `tf.keras.losses.SparseCategoricalCrossentropy` 作为损失函数。同时，我们还定义了一个 `BahdanauAttention` 层来实现注意力机制。