如何使训练和测试测试正确的尺寸以配合在模型中嵌入埃尔莫

Question

我有错误，而与嵌合训练集尺寸的x_tr =（43163，50）的毛毛嵌入模型，和y_tr =（43163，50，1）为：

InvalidArgumentError: Incompatible shapes: [1600] vs. [32,50]
     [[{{node metrics/acc/Equal}} = Equal[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](metrics/acc/Reshape, metrics/acc/Cast)]].

如何解决这个问题？

我试图通过使训练样本整除批量大小来解决。

training set for fitting the model:

X_tr=np.array(X_tr)
print(X_tr.shape)
y_tr = np.array(y_tr).reshape(len(y_tr), max_len, 1)
print(y_tr.shape)
(43163, 50)
(43163, 50, 1)

making the model :

input_text = Input(shape=(max_len,), dtype=tf.string)
embedding = Lambda(ElmoEmbedding, output_shape=(None, 1024))(input_text)
x = Bidirectional(LSTM(units=512, return_sequences=True,
                       recurrent_dropout=0.2, dropout=0.2))(embedding)
x_rnn = Bidirectional(LSTM(units=512, return_sequences=True,
                           recurrent_dropout=0.2, dropout=0.2))(x)
x = add([x, x_rnn])  # residual connection to the first biLSTM
out = TimeDistributed(Dense(n_tags, activation="softmax"))(x)
model = Model(input_text, out)

compiling the model:

model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])

fitting the model:

fit_model = model.fit(np.array(X_tr), np.array(y_tr).reshape(len(y_tr), max_len, 1), validation_split=0.1,
                    batch_size=batch_size, epochs=5, verbose=1)

错误：

    InvalidArgumentError: Incompatible shapes: [1600] vs. [32,50]
     [[{{node metrics/acc/Equal}} = Equal[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](metrics/acc/Reshape, metrics/acc/Cast)]]

Expected result could be:
Train on 38816 samples, validate on 4320 samples
Epoch 1/5
38816/38816 [==============================] - 433s 11ms/step - loss: 0.0625 - acc: 0.9818 - val_loss: 0.0459 - val_acc: 0.9858
Epoch 2/5
38816/38816 [==============================] - 430s 11ms/step - loss: 0.0404 - acc: 0.9869 - val_loss: 0.0421 - val_acc: 0.9865
Epoch 3/5
38816/38816 [==============================] - 429s 11ms/step - loss: 0.0334 - acc: 0.9886 - val_loss: 0.0426 - val_acc: 0.9868
Epoch 4/5
38816/38816 [==============================] - 429s 11ms/step - loss: 0.0275 - acc: 0.9904 - val_loss: 0.0431 - val_acc: 0.9868
Epoch 5/5
38816/38816 [==============================] - 430s 11ms/step - loss: 0.0227 - acc: 0.9920 - val_loss: 0.0461 - val_acc: 0.9867

Answer 1

解决：我已经通过去除指标= [“精度”]偏偏这个精度指标的游戏错误我仍然不知道解决了这个问题。如果有人知道请帮我