TensorFlow错误：ValueError：形状（128，100）和（128，100，139）不兼容
我尝试将功能API用于模型，但我不明白为什么有错误
ValueError: Shapes (128, 100) and (128, 100, 139) are incompatible
：
input_tensor = Input(batch_input_shape=(batch_size,None))
x = Embedding(vocab_size, embed_dim)(input_tensor)
x = LSTM(rnn_neurons4, return_sequences=True, stateful=True)(x)
output_tensor = Dense(vocab_size, activation='softmax')(x)
model = Model(input_tensor, output_tensor)
model.summary()
Adam = tf.keras.optimizers.Adam(learning_rate=0.0001)
model.compile(optimizer=Adam, loss="categorical_crossentropy", metrics=['accuracy'])
适合代码：
epochs = 1000
early_stop = EarlyStopping(monitor='loss', patience=25)
try:
model.fit(dataset,epochs=epochs, callbacks=[early_stop])
model.save('train.h5')
except KeyboardInterrupt:
model.save('train.h5')
I try to use Functional API for my model, but i don't understand why i have error:
ValueError: Shapes (128, 100) and (128, 100, 139) are incompatible
My code:
input_tensor = Input(batch_input_shape=(batch_size,None))
x = Embedding(vocab_size, embed_dim)(input_tensor)
x = LSTM(rnn_neurons4, return_sequences=True, stateful=True)(x)
output_tensor = Dense(vocab_size, activation='softmax')(x)
model = Model(input_tensor, output_tensor)
model.summary()
Adam = tf.keras.optimizers.Adam(learning_rate=0.0001)
model.compile(optimizer=Adam, loss="categorical_crossentropy", metrics=['accuracy'])
fit code:
epochs = 1000
early_stop = EarlyStopping(monitor='loss', patience=25)
try:
model.fit(dataset,epochs=epochs, callbacks=[early_stop])
model.save('train.h5')
except KeyboardInterrupt:
model.save('train.h5')
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我使用sparse_categorical_crossential创建自己的功能，然后添加型号。
I create my own function with sparse_categorical_crossential and add in model.compile