![](/img/trans.png)
[英]InvalidArgumentError (see above for traceback): Input to reshape is a tensor with 35000 values, but the requested shape requires a multiple of 7500
[英]Input to reshape is a tensor with 'batch_size' values, but the requested shape requires a multiple of 'n_features'
我試圖引起自己的注意 model 我在這里找到了示例代碼: https://www.kaggle.com/takuok/bidirectional-lstm-and-attention-lb-0-043
當我不加修改地運行它時它工作得很好。
但是我自己的數據只包含數值,我不得不更改示例代碼。
所以我刪除了示例代碼中的嵌入部分,另外,這就是我修復的。
xtr = np.reshape(xtr, (xtr.shape[0], 1, xtr.shape[1]))
# xtr.shape() = (n_sample_train, 1, 150), y.shape() = (n_sample_train, 6)
xte = np.reshape(xte, (xte.shape[0], 1, xte.shape[1]))
# xtr.shape() = (n_sample_test, 1, 150)
model = BidLstm(maxlen, max_features)
model.compile(loss='binary_crossentropy', optimizer='adam',
metrics=['accuracy'])
我的 BidLstm 函數看起來像,
def BidLstm(maxlen, max_features):
inp = Input(shape=(1,150))
#x = Embedding(max_features, embed_size, weights=[embedding_matrix], trainable=False)(inp) -> I don't need embedding since my own data is numeric.
x = Bidirectional(LSTM(300, return_sequences=True, dropout=0.25,
recurrent_dropout=0.25))(inp)
x = Attention(maxlen)(x)
x = Dense(256, activation="relu")(x)
x = Dropout(0.25)(x)
x = Dense(6, activation="sigmoid")(x)
model = Model(inputs=inp, outputs=x)
return model
它說,
InvalidArgumentErrorTraceback (most recent call last)
<ipython-input-62-929955370368> in <module>
29
30 early = EarlyStopping(monitor="val_loss", mode="min", patience=1)
---> 31 model.fit(xtr, y, batch_size=128, epochs=15, validation_split=0.1, callbacks=[early])
32 #model.fit(xtr, y, batch_size=256, epochs=1, validation_split=0.1)
33
/usr/local/lib/python3.5/dist-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
1037 initial_epoch=initial_epoch,
1038 steps_per_epoch=steps_per_epoch,
-> 1039 validation_steps=validation_steps)
1040
1041 def evaluate(self, x=None, y=None,
/usr/local/lib/python3.5/dist-packages/keras/engine/training_arrays.py in fit_loop(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)
197 ins_batch[i] = ins_batch[i].toarray()
198
--> 199 outs = f(ins_batch)
200 outs = to_list(outs)
201 for l, o in zip(out_labels, outs):
/usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py in __call__(self, inputs)
2713 return self._legacy_call(inputs)
2714
-> 2715 return self._call(inputs)
2716 else:
2717 if py_any(is_tensor(x) for x in inputs):
/usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py in _call(self, inputs)
2673 fetched = self._callable_fn(*array_vals, run_metadata=self.run_metadata)
2674 else:
-> 2675 fetched = self._callable_fn(*array_vals)
2676 return fetched[:len(self.outputs)]
2677
/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py in __call__(self, *args, **kwargs)
1437 ret = tf_session.TF_SessionRunCallable(
1438 self._session._session, self._handle, args, status,
-> 1439 run_metadata_ptr)
1440 if run_metadata:
1441 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
526 None, None,
527 compat.as_text(c_api.TF_Message(self.status.status)),
--> 528 c_api.TF_GetCode(self.status.status))
529 # Delete the underlying status object from memory otherwise it stays alive
530 # as there is a reference to status from this from the traceback due to
InvalidArgumentError: Input to reshape is a tensor with 128 values, but the requested shape requires a multiple of 150
[[{{node attention_16/Reshape_2}}]]
[[{{node loss_5/mul}}]]
我認為損失 function 有問題在這里說: 重塑的輸入是具有 2 *“batch_size”值的張量,但請求的形狀具有“batch_size”
但我不知道要修復哪個部分。
我的 keras 和 tensorflow 版本是 2.2.4 和 1.13.0-rc0
請幫忙。 謝謝。
編輯 1
我已經更改了批量大小,如 keras 所說,是 150 的倍數(batch_size = 150)。 比它報告
Train on 143613 samples, validate on 15958 samples
Epoch 1/15
143400/143613 [============================>.] - ETA: 0s - loss: 0.1505 - acc: 0.9619
InvalidArgumentError: Input to reshape is a tensor with 63 values, but the requested shape requires a multiple of 150
[[{{node attention_18/Reshape_2}}]]
[[{{node metrics_6/acc/Mean_1}}]]
細節和以前一樣。 我應該怎么辦?
您的輸入形狀必須是(150,1)
。
LSTM 形狀是(batch, steps, features)
。 僅使用 1 步的 LSTM 是沒有意義的。 (除非您使用帶有stateful=True
的自定義訓練循環,這不是您的情況)。
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