ValueError: in user code: File "D:\ANACONDA3\lib\site-packages\keras\engine\training.py", line 1160, in train_function * return step_function(self, iterator) File "D:\ANACONDA3\lib\site-packages\keras\engine\training.py", line 1146, in step_function ** outputs = model.distribute_strategy.run(run_step, args=(data,)) File "D:\ANACONDA3\lib\site-packages\keras\engine\training.py", line 1135, in run_step ** outputs = model.train_step(data) File "D:\ANACONDA3\lib\site-packages\keras\engine\training.py", line 994, in train_step loss = self.compute_loss(x, y, y_pred, sample_weight) File "D:\ANACONDA3\lib\site-packages\keras\engine\training.py", line 1052, in compute_loss return self.compiled_loss( File "D:\ANACONDA3\lib\site-packages\keras\engine\compile_utils.py", line 265, in __call__ loss_value = loss_obj(y_t, y_p, sample_weight=sw) File "D:\ANACONDA3\lib\site-packages\keras\losses.py", line 152, in __call__ losses = call_fn(y_true, y_pred) File "D:\ANACONDA3\lib\site-packages\keras\losses.py", line 272, in call ** return ag_fn(y_true, y_pred, **self._fn_kwargs) File "D:\ANACONDA3\lib\site-packages\keras\losses.py", line 1990, in categorical_crossentropy return backend.categorical_crossentropy( File "D:\ANACONDA3\lib\site-packages\keras\backend.py", line 5529, in categorical_crossentropy target.shape.assert_is_compatible_with(output.shape) ValueError: Shapes (None, 1) and (None, 62) are incompatible

keras报错：ValueError: Cannot create group in read only mode

keras保存模型时，使用的是m.save_weights，只保存了模型的权重而没有结构，直接使用keras.models.load_model就会报错。所以，可以重新构建相同结构的模型，（m.save_weights）使用m.load_weights；或者如上m.save，...

keras-grad-cam：带有keras的Grad-CAM的实现

在Keras中实现Grad-CAM 梯度类激活图是用于深度学习网络的可视化技术。参见论文： : 该论文的作者... 用法： python grad-cam.py 例子示例图片： '拳击手'（在keras中为243或242） '老虎猫'（在keras中为283或282）

Python解析json之ValueError: Expecting property name enclosed in double quotes: line 1 column 2（char 1）

前言在Python中提供了json包来方便快捷的解析json字串的转换过程，但是碰到了一个比较奇怪的问题，就是不太正确的json串如何来解析？ 1. 问题的提出 ... File /Users/tonychen/anaconda2/lib/python2.7/json/__

File "C:\Users\200511672\Anaconda3\lib\site-packages\keras\engine\base_layer.py", line 305, in assert_input_compatibility str(K.ndim(x))) ValueError: Input 0 is incompatible with layer conv2d_1: expected ndim=4, found ndim=3

conv_layer = Conv2D(filters=32, kernel_size=(3, 3), padding='same', activation='relu')(input_tensor) 这里我们使用 tf.expand_dims 函数将一个 3D 张量扩展为 4D 张量，然后将其作为卷积层的输入张量。...

def create_LSTM_model(): # instantiate the model model = Sequential() model.add(Input(shape=(X_train.shape[1], X_train.shape[2]5))) model.add(Reshape((X_train.shape[1], 1,X_train.shape[2], 5))) # cnn1d Layers model.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu', padding='same', return_sequences=True)) model.add(Flatten()) model.add(Dropout(0.5)) model.add(RepeatVector(1)) # 添加lstm层 model.add(LSTM(64, activation = 'relu', return_sequences=True)) model.add(Dropout(0.5)) #添加注意力层 model.add(LSTM(64, activation = 'relu', return_sequences=False)) # 添加dropout model.add(Dropout(0.5)) model.add(Dense(128)) # 输出层 model.add(Dense(1, name='Output')) # 编译模型 model.compile(optimizer='adam', loss='mse', metrics=['mae']) return model # lstm network model = create_LSTM_model() # summary print(model.summary())修改代码，解决ValueError: in user code: File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\training.py", line 1284, in train_function return step_function(self, iterator) File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\training.py", line 1268, in step_function outputs = model.distribute_strategy.run(run_step, args=(data,)) File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\training.py", line 1249, in run_step outputs = model.train_step(data) File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\training.py", line 1050, in train_step y_pred = self(x, training=True) File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler raise e.with_traceback(filtered_tb) from None File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\input_spec.py", line 298, in assert_input_compatibility raise ValueError( ValueError: Input 0 of layer "sequential_71" is incompatible with the layer: expected shape=(None, 10, 5), found shape=(None, 10, 1, 1, 5)

model.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu', padding='same', return_sequences=True)) model.add(Flatten()) model.add(Dropout(0.5)) model.add(RepeatVector(1)) # 添加lstm...

def create_LSTM_model(): # instantiate the model model = Sequential() model.add(Input(shape=(X_train.shape[1], X_train.shape[2]))) model.add(Reshape((X_train.shape[1], 1, X_train.shape[2], 1))) # cnn1d Layers model.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu', padding='same', return_sequences=True)) model.add(Flatten()) model.add(Dropout(0.5)) model.add(RepeatVector(1)) # 添加lstm层 model.add(LSTM(64, activation = 'relu', return_sequences=True)) model.add(Dropout(0.5)) #添加注意力层 model.add(LSTM(64, activation = 'relu', return_sequences=False)) # 添加dropout model.add(Dropout(0.5)) model.add(Dense(128)) # 输出层 model.add(Dense(1, name='Output')) # 编译模型 model.compile(optimizer='adam', loss='mse', metrics=['mae']) return model # lstm network model = create_LSTM_model() # summary print(model.summary())修改该代码，解决ValueError: in user code: File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\training.py", line 1284, in train_function * return step_function(self, iterator) File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\training.py", line 1268, in step_function outputs = model.distribute_strategy.run(run_step, args=(data,)) File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\training.py", line 1249, in run_step outputs = model.train_step(data) File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\training.py", line 1050, in train_step y_pred = self(x, training=True) File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler raise e.with_traceback(filtered_tb) from None File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\layers\reshaping\reshape.py", line 118, in _fix_unknown_dimension raise ValueError(msg) ValueError: Exception encountered when calling layer 'reshape_51' (type Reshape). total size of new array must be unchanged, input_shape = [10, 1, 1, 5], output_shape = [10, 1, 1, 1] Call arguments received by layer 'reshape_51' (type Reshape): • inputs=tf.Tensor(shape=(None, 10, 1, 1, 5), dtype=float32)问题

该代码中的问题在于 Reshape 层的输出形状与后续层的输入形状不一致，导致了 ValueError。具体来说，Reshape 层将输入形状从 (batch_size, time_steps, features) 转换为了 (batch_size, time_steps, 1, features, 1...

用上述模型，产生如下错误： File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\engine\base_layer.py:338 in assert_input_compatibility raise ValueError('Input ' + str(input_index) + ValueError: Input 0 is incompatible with layer bidirectional_1: expected ndim=3, found ndim=4

在这个错误信息中，模型的一个双向循环层（bidirectional_1）期望输入的张量维度是3，但是实际输入的张量维度是4。可能是因为你的输入数据维度不正确，或者是因为你的模型架构中有一个不匹配的层。你可以检查你的...

trainX Y shape is: (4755, 48, 23) (4755, 60, 23) (4755, 5, 1) Hx= 1 Traceback (most recent call last): File ~\anaconda4\envs\tensorflow\lib\site-packages\spyder_kernels\py3compat.py:356 in compat_exec exec(code, globals, locals) File d:\lstnet_keras-master\train_batch_a391.py:172 model,normalize = startTrainMult_1H(df,name,config) File D:\lstnet_keras-master\lstm\LSTNet_Interface_a391.py:400 in startTrainMult_1H model = trainModel(trainX1, trainX2 , trainY, config) File D:\lstnet_keras-master\lstm\LSTNet_Interface_a391.py:323 in trainModel model = LSTNetAttention(trainX1,trainX2,trainY,config) File D:\lstnet_keras-master\lstm\LSTNet_Interface_a391.py:297 in LSTNetAttention res = add([concat,z]) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\layers\merge.py:555 in add return Add(**kwargs)(inputs) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\backend\tensorflow_backend.py:75 in symbolic_fn_wrapper return func(*args, **kwargs) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\engine\base_layer.py:463 in call self.build(unpack_singleton(input_shapes)) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\layers\merge.py:90 in build output_shape = self._compute_elemwise_op_output_shape(output_shape, File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\layers\merge.py:59 in _compute_elemwise_op_output_shape raise ValueError('Operands could not be broadcast ' ValueError: Operands could not be broadcast together with shapes (23, 32) (5,)

这个错误是由于在使用 Keras 进行模型训练时，两个张量的形状无法进行广播运算所导致的。具体来说，在您的代码中，您使用了 Keras 的 Merge 层中的 Add 层，将两个张量进行相加。但是，这两个张量的形状不匹配，无法...

model = LSTNetAttention(trainX1,trainX2,trainY,config) File D:\lstnet_keras-master\lstm\LSTNet_Interface_a38.py:451 in LSTNetAttention context_vector1 = Dot(axes=(2, 1))([attention_weights1, input1]) File ~\anaconda4\envs\tensorflow\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:897 in call self._maybe_build(inputs) File ~\anaconda4\envs\tensorflow\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:2416 in _maybe_build self.build(input_shapes) # pylint:disable=not-callable File ~\anaconda4\envs\tensorflow\lib\site-packages\tensorflow\python\keras\utils\tf_utils.py:316 in wrapper output_shape = fn(instance, input_shape) File ~\anaconda4\envs\tensorflow\lib\site-packages\tensorflow\python\keras\layers\merge.py:674 in build raise ValueError('Dimension incompatibility ' ValueError: Dimension incompatibility 1 != 48. Layer shapes: (None, 48, 1), (None, 48, 21). Chosen axes: 2, 1

这意味着您要对第一个张量的第二个维度和第二个张量的第一个维度进行点积操作，但是这两个维度的大小不同，分别为 1 和 48，所以会抛出 ValueError 异常。您需要检查一下您的代码，特别是输入张量的构建部分，确保...

[ WARN:0@2804.030] global loadsave.cpp:244 cv::findDecoder imread_('C:\Users\CXY\PycharmProjects\pythonProject\data\test'): can't open/read file: check file path/integrity [ WARN:0@2804.030] global loadsave.cpp:244 cv::findDecoder imread_('C:\Users\CXY\PycharmProjects\pythonProject\data\train'): can't open/read file: check file path/integrity Traceback (most recent call last): File "D:\ANACONDA3\lib\site-packages\IPython\core\interactiveshell.py", line 3505, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-23-66d5a743ade9>", line 48, in <module> model.fit(train_data, train_labels, epochs=10, batch_size=32) File "D:\ANACONDA3\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler raise e.with_traceback(filtered_tb) from None File "D:\ANACONDA3\lib\site-packages\tensorflow\python\framework\constant_op.py", line 102, in convert_to_eager_tensor return ops.EagerTensor(value, ctx.device_name, dtype) ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type NoneType).出现以上错误请指出并修正

3. 检查训练数据和标签：确保训练数据和标签中没有空值（None）。如果存在空值，可以考虑删除这些样本或者进行适当的处理，例如填充缺失值或使用其他合理的方式来处理空值。 4. 确保相关库和依赖项已正确安装：确保...

ValueError Traceback (most recent call last) <ipython-input-56-571552a9fb46> in <module>() ----> 1 prediction = model.predict(number_data_normalize) 2 print(prediction) 3 np.max(prediction) 4 np.argmax(prediction) D:\360downloads\Anaconda\envs\tensorflow_cpu\lib\site-packages\keras\engine\training.py in predict(self, x, batch_size, verbose, steps) 1147 'argument.') 1148 # Validate user data. -> 1149 x, _, _ = self._standardize_user_data(x) 1150 if self.stateful: 1151 if x[0].shape[0] > batch_size and x[0].shape[0] % batch_size != 0: D:\360downloads\Anaconda\envs\tensorflow_cpu\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size) 749 feed_input_shapes, 750 check_batch_axis=False, # Don't enforce the batch size. --> 751 exception_prefix='input') 752 753 if y is not None: D:\360downloads\Anaconda\envs\tensorflow_cpu\lib\site-packages\keras\engine\training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix) 126 ': expected ' + names[i] + ' to have ' + 127 str(len(shape)) + ' dimensions, but got array ' --> 128 'with shape ' + str(data_shape)) 129 if not check_batch_axis: 130 data_shape = data_shape[1:] ValueError: Error when checking input: expected conv2d_1_input to have 4 dimensions, but got array with shape (1, 784)

这个错误是因为模型期望输入有四个维度，而你的输入数据只有两个维度。在使用CNN进行图像预测时，通常需要将图像转换为四维张量，形状为(batch_size, height, width, channels)。在你的情况下，你的输入数据只有一...

Traceback (most recent call last): File "train.py", line 294, in <module> train() File "train.py", line 177, in train pred = get_model(pointclouds_phs[-1], is_training_phs[-1], bn_decay=bn_decay) File "/data/hxl/TDGCNN6sagan/tensorflow/sem_seg/model.py", line 569, in get_model self_att_1 = SelfAttention(out111) File "/data/hxl/TDGCNN6sagan/tensorflow/sem_seg/model.py", line 505, in init self.q_map = tf.keras.layers.Conv1D(self.out_channel, 1, use_bias=False) File "/root/anaconda3/envs/py37tf241/lib/python3.7/site-packages/tensorflow/python/keras/layers/convolutional.py", line 512, in init **kwargs) File "/root/anaconda3/envs/py37tf241/lib/python3.7/site-packages/tensorflow/python/keras/layers/convolutional.py", line 164, in init self._validate_init() File "/root/anaconda3/envs/py37tf241/lib/python3.7/site-packages/tensorflow/python/keras/layers/convolutional.py", line 175, in _validate_init self.groups, self.filters)) ValueError: The number of filters must be evenly divisible by the number of groups. Received: groups=1, filters=Tensor("transpose_2:0", shape=(24, 64, 4096), dtype=float32, device=/device:GPU:0)

这个错误是由于在模型的某个层中，输入的 filters 参数的形状不符合要求导致的。根据错误信息，可以看到 filters 的形状为 (24, 64, 4096)，而 groups 参数为 1，这意味着 filters 的数量必须可以被 groups 整除。...

ValueError Traceback (most recent call last) Cell In[17], line 5 3 model.load_weights("unet_membrane.hdf5") 4 #results = model.predict_generator(testGene,67,verbose=1) ----> 5 results = model.predict(testGene,verbose=1) 6 saveResult("data/results",results) File d:\Software\Anaconda\Ana_qinghua\lib\site-packages\keras\utils\traceback_utils.py:70, in filter_traceback..error_handler(*args, **kwargs) 67 filtered_tb = _process_traceback_frames(e.traceback) 68 # To get the full stack trace, call: 69 # tf.debugging.disable_traceback_filtering() ---> 70 raise e.with_traceback(filtered_tb) from None 71 finally: 72 del filtered_tb File ~\AppData\Local\Temp\__autograph_generated_file8h3jf8qv.py:15, in outer_factory..inner_factory..tf__predict_function(iterator) 13 try: 14 do_return = True ---> 15 retval_ = ag__.converted_call(ag__.ld(step_function), (ag.ld(self), ag.ld(iterator)), None, fscope) 16 except: 17 do_return = False ValueError: in user code: ... Call arguments received by layer 'model_7' (type Functional): • inputs=tf.Tensor(shape=(None, None, None), dtype=float32) • training=False • mask=None Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...

这个错误信息是在使用 Keras 模型进行预测时出现的。它表明预测过程中出现了错误，具体原因是模型的某个名为 "model_7" 的 Functional 层接收到了不符合要求的调用参数。该层要求接收一个形状为 (None, None, None) ...

上述代码报错：Using TensorFlow backend. 2023-06-02 17:07:57.266774: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found 2023-06-02 17:07:57.267572: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. Loading data... Traceback (most recent call last): File "C:/Users/85448/PycharmProjects/LightGBM_text/deep2.py", line 12, in <module> (x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features) File "C:\Users\85448\anaconda3\envs\tensorflow37\lib\site-packages\keras\datasets\imdb.py", line 54, in load_data x_train, labels_train = f['x_train'], f['y_train'] File "C:\Users\85448\anaconda3\envs\tensorflow37\lib\site-packages\numpy\lib\npyio.py", line 256, in getitem pickle_kwargs=self.pickle_kwargs) File "C:\Users\85448\anaconda3\envs\tensorflow37\lib\site-packages\numpy\lib\format.py", line 743, in read_array raise ValueError("Object arrays cannot be loaded when " ValueError: Object arrays cannot be loaded when allow_pickle=False

from keras.layers import Dense, Embedding, LSTM # 加载数据集 max_features = 20000 maxlen = 80 batch_size = 32 print('Loading data...') (x_train, y_train), (x_test, y_test) = imdb.load_data(num_words...

--------------------------------------------------------------------------- ValueError Traceback (most recent call last) Input In [27], in <cell line: 11>() 9 model.add(LSTM(units=32, input_shape=(sequence_length, 4))) 10 model.add(Dropout(0.2)) ---> 11 model.add(LSTM(units=32)) 12 model.add(Dense(units=1, activation='sigmoid')) 14 # 编译模型 File ~/anaconda3/lib/python3.9/site-packages/tensorflow/python/trackable/base.py:204, in no_automatic_dependency_tracking.<locals>._method_wrapper(self, *args, **kwargs) 202 self._self_setattr_tracking = False # pylint: disable=protected-access 203 try: --> 204 result = method(self, *args, **kwargs) 205 finally: 206 self._self_setattr_tracking = previous_value # pylint: disable=protected-access File ~/anaconda3/lib/python3.9/site-packages/keras/src/utils/traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs) 67 filtered_tb = _process_traceback_frames(e.traceback) 68 # To get the full stack trace, call: 69 # tf.debugging.disable_traceback_filtering() ---> 70 raise e.with_traceback(filtered_tb) from None 71 finally: 72 del filtered_tb File ~/anaconda3/lib/python3.9/site-packages/keras/src/engine/input_spec.py:235, in assert_input_compatibility(input_spec, inputs, layer_name) 233 ndim = shape.rank 234 if ndim != spec.ndim: --> 235 raise ValueError( 236 f'Input {input_index} of layer "{layer_name}" ' 237 "is incompatible with the layer: " 238 f"expected ndim={spec.ndim}, found ndim={ndim}. " 239 f"Full shape received: {tuple(shape)}" 240 ) 241 if spec.max_ndim is not None: 242 ndim = x.shape.rank ValueError: Input 0 of layer "lstm_8" is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 32)

这个错误提示说明你的 LSTM 层期望输入的张量维度为 3，但是实际的输入张量维度只有 2。具体来说，LSTM 层期望的输入张量维度应该为 (batch_size, sequence_length, input_dim)，其中 batch_size 表示输入的样本数目...

ValueError Traceback (most recent call last) <ipython-input-54-536a68c200e5> in <module> 52 return model 53 # lstm network ---> 54 model = create_LSTM_model(X_train,n_steps,n_length, n_features) 55 # summary 56 print(model.summary()) <ipython-input-54-536a68c200e5> in create_LSTM_model(X_train, n_steps, n_length, n_features) 22 X_train = X_train.reshape((X_train.shape[0], n_steps, 1, n_length, n_features)) 23 ---> 24 model.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu', 25 input_shape=(n_steps, 1, n_length, n_features))) 26 model.add(Flatten()) ~\anaconda3\lib\site-packages\tensorflow\python\trackable\base.py in _method_wrapper(self, *args, **kwargs) 203 self._self_setattr_tracking = False # pylint: disable=protected-access 204 try: --> 205 result = method(self, *args, **kwargs) 206 finally: 207 self._self_setattr_tracking = previous_value # pylint: disable=protected-access ~\anaconda3\lib\site-packages\keras\utils\traceback_utils.py in error_handler(*args, **kwargs) 68 # To get the full stack trace, call: 69 # tf.debugging.disable_traceback_filtering() ---> 70 raise e.with_traceback(filtered_tb) from None 71 finally: 72 del filtered_tb ~\anaconda3\lib\site-packages\keras\engine\input_spec.py in assert_input_compatibility(input_spec, inputs, layer_name) 233 ndim = shape.rank 234 if ndim != spec.ndim: --> 235 raise ValueError( 236 f'Input {input_index} of layer "{layer_name}" ' 237 "is incompatible with the layer: " ValueError: Input 0 of layer "conv_lstm2d_12" is incompatible with the layer: expected ndim=5, found ndim=3. Full shape received: (None, 10, 5)解决该错误

这个层期望的输入张量维度是 5，而实际输入张量的维度是 3。具体来说，期望的维度是 (batch_size, timesteps, channels, rows, cols)，而实际的维度是 (batch_size, timesteps, features)。这个问题的原因可能是在...

ValueError: Could not find a format to read the specified file in mode ‘i’

此类问题一般跟python的imageio模块有关，解决办法一可尝试加个plugin image = io.imread(filename,plugin='matplotlib') 或者加个pilmode imageio.imread(filename,pilmode=RGB) 参考链接一 ...

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File "C:\Users\200511672\Anaconda3\lib\site-packages\keras\engine\base_layer.py", line 305, in assert_input_compatibility str(K.ndim(x))) ValueError: Input 0 is incompatible with layer conv2d_1: expected ndim=4, found ndim=3

用上述模型，产生如下错误 ： File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\engine\base_layer.py:338 in assert_input_compatibility raise ValueError('Input ' + str(input_index) + ValueError: Input 0 is incompatible with layer bidirectional_1: expected ndim=3, found ndim=4

ValueError: Could not find a format to read the specified file in mode ‘i’

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用上述模型，产生如下错误： File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\engine\base_layer.py:338 in assert_input_compatibility raise ValueError('Input ' + str(input_index) + ValueError: Input 0 is incompatible with layer bidirectional_1: expected ndim=3, found ndim=4