如何确定正确的训练和测试维度以适合elmo嵌入模型

Question

在用尺寸为x_tr =（43163，50）和y_tr =的训练集拟合elmo嵌入模型时，我出错了 （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)]].

如何解决此错误？

我试图通过将训练样本除以批次大小来解决。

用于拟合模型的训练集：

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)

建立模型：

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)

编译模型：

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

拟合模型：

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

已解决： 我已通过删除指标= ['准确性']解决了此问题 但是为什么我仍然不知道为什么这种准确性衡量游戏错误。 如果有人知道，请帮帮我