所以我试图建立一个词嵌入模型，但我一直收到这个错误。在训练过程中，准确率没有变化，val_loss 保持“nan”
数据的原始形状是
x.shape, y.shape
((94556,), (94556, 2557))
然后我重塑它：
xr= np.asarray(x).astype('float32').reshape((-1,1))
yr= np.asarray(y).astype('float32').reshape((-1,1))
((94556, 1), (241779692, 1))
然后我通过我的模型运行它
model = Sequential()
model.add(Embedding(2557, 64, input_length=150, embeddings_initializer='glorot_uniform'))
model.add(Flatten())
model.add(Reshape((64,), input_shape=(94556, 1)))
model.add(Dense(512, activation='sigmoid'))
model.add(Dense(128, activation='sigmoid'))
model.add(Dense(64, activation='relu'))
model.add(Dense(10, activation='sigmoid'))
model.add(Dense(1, activation='relu'))
# compile the mode
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# summarize the model
print(model.summary())
plot_model(model, show_shapes = True, show_layer_names=False)
训练后，我得到了每个时期的恒定准确度和 val_loss nan
history=model.fit(xr, yr, epochs=20, batch_size=32, validation_split=3/9)
Epoch 1/20
WARNING:tensorflow:Model was constructed with shape (None, 150) for input Tensor("embedding_6_input:0", shape=(None, 150), dtype=float32), but …