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Python - Shapes (None, 1) and (None, 25) are incompatible of CNN model

I'm trying to train the model but I am stuck with this error
ValueError: Shapes (None, 1) and (None, 24) are incompatible
My code
model = models.Sequential()
model.add(layers.Conv2D(32, kernel_size=(3, 3), activation='relu',input_shape=(28, 28, 1)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
model.add(layers.Dropout(0.25))
model.add(layers.Flatten())
model.add(layers.Dense(128, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(25, activation=tf.nn.softmax))
model.summary()
Model Summary
Model: "sequential_6"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_18 (Conv2D) (None, 26, 26, 32) 320
conv2d_19 (Conv2D) (None, 24, 24, 64) 18496
conv2d_20 (Conv2D) (None, 22, 22, 128) 73856
max_pooling2d_8 (MaxPooling (None, 11, 11, 128) 0
2D)
dropout_8 (Dropout) (None, 11, 11, 128) 0
flatten_5 (Flatten) (None, 15488) 0
dense_10 (Dense) (None, 128) 1982592
dropout_9 (Dropout) (None, 128) 0
dense_11 (Dense) (None, 25) 3225
=================================================================
Total params: 2,078,489
Trainable params: 2,078,489
Non-trainable params: 0
Complier
model.compile(loss=tf.keras.losses.categorical_crossentropy,
optimizer=tf.keras.optimizers.Adadelta(), metrics=['accuracy'])
Fit the model
model.fit(train_img, train_y_values, batch_size=64, epochs=5,
verbose=1, validation_data=(test_img, test_y_values))
Error
Epoch 1/5
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-61-1ace0d4f685d> in <module>
1 model.fit(train_img, train_y_values, batch_size=64, epochs=5,
----> 2 verbose=1, validation_data=(test_img, test_y_values))
1 frames
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py in tf__train_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:
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1051, in train_function *
return step_function(self, iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1040, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1030, in run_step **
outputs = model.train_step(data)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 890, in train_step
loss = self.compute_loss(x, y, y_pred, sample_weight)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 949, in compute_loss
y, y_pred, sample_weight, regularization_losses=self.losses)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py", line 201, in __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 139, in __call__
losses = call_fn(y_true, y_pred)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 243, in call **
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 1788, in categorical_crossentropy
y_true, y_pred, from_logits=from_logits, axis=axis)
File "/usr/local/lib/python3.7/dist-packages/keras/backend.py", line 5119, in categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
ValueError: Shapes (None, 1) and (None, 24) are incompatible
train_img shape = (27455, 28, 28)
train_y_value shape = (27455,)
test_img shape = (7172, 28, 28)
test_y_values shape = (7172,)
Somebody help me please

The problem comes from a mismatch of the last layer of your model (None, 25) and the labels (None, 1).
This can be fixed by changing the loss function to "sparse_categorical_crossentropy"
See What is the difference between sparse_categorical_crossentropy and categorical_crossentropy? for more details.

AssertionError: with Keras Functional API

The code I'm trying to run to create a Neural Network model to predict some damping ratio and added mass coefficient. I have 3 inputs of the model (float numbers) and 4 outputs of the model (float numbers).
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.optimizers import SGD
from keras.models import Model
from keras.layers import Input
from keras.layers import Dense
from keras.layers import Concatenate
from sklearn.model_selection import train_test_split
from Reasearch_project_Code import *
"""Create model"""
Input1 = Input(shape=(1),name="Input1")
Input2 = Input(shape=(1),name="Input2")
Input3 = Input(shape=(1),name="Input3")
Inputs = Concatenate(axis=-1,name="Inputs")([Input1,Input2,Input3])
hidden1 = Dense(100, activation='relu',name="Hidden1")(Inputs)
hidden2 = Dense(100, activation='relu',name="Hidden2")(hidden1)
# Output layer
output1 = Dense(1, activation='linear',name="Output_1")(hidden2)
output2 = Dense(1, activation='linear',name="Output_2")(hidden2)
output3 = Dense(1, activation='linear',name="Output_3")(hidden2)
output4 = Dense(1, activation='linear',name="Output_4")(hidden2)
# output
model = Model(inputs=[Input1,Input2,Input3], outputs=[output1, output2, output3, output4])
# summarize layers
print(model.summary())
opt = SGD(0.001)
model.compile(loss='mean_squared_error', optimizer=opt)
"""Load Data"""
dpath="C:\\Users\\jules\\OneDrive - University of Southampton\\Documents\\Research Project\\5- Codes"
R=RP(dpath)
data="Data/Test3/Test3.csv"
labelss=np.genfromtxt(data,skip_header=1,usecols=(1,2,3),delimiter=",")
sampless=np.genfromtxt(data,skip_header=1,usecols=(4,5,6,7),delimiter=",")
"""scaled data"""
scaler=MinMaxScaler(feature_range=(0,1))
samples = scaler.fit_transform(sampless)
labels = scaler.fit_transform(labelss)
"""split data"""
labels_train, labels_test, samples_train , samples_test= train_test_split(labels,samples,train_size=0.9,random_state=42)
print(f"labels_train:{labels_train.shape}")
print(f"labels_test:{labels_test.shape}")
print(f"samples_train:{samples_train.shape}")
print(f"samples_test:{samples_test.shape}")
history = model.fit(labels_train, samples_train, validation_data=(labels_test, samples_test), epochs=200,batch_size=20, verbose=1)
shape of the data:
labels_train:(6753, 3)
labels_test:(751, 3)
samples_train:(6753, 4)
samples_test:(751, 4)
model:
Model: "functional_115"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
Input1 (InputLayer) [(None, 1)] 0
__________________________________________________________________________________________________
Input2 (InputLayer) [(None, 1)] 0
__________________________________________________________________________________________________
Input3 (InputLayer) [(None, 1)] 0
__________________________________________________________________________________________________
Inputs (Concatenate) (None, 3) 0 Input1[0][0]
Input2[0][0]
Input3[0][0]
__________________________________________________________________________________________________
Hidden1 (Dense) (None, 100) 400 Inputs[0][0]
__________________________________________________________________________________________________
Hidden2 (Dense) (None, 100) 10100 Hidden1[0][0]
__________________________________________________________________________________________________
Output_1 (Dense) (None, 1) 101 Hidden2[0][0]
__________________________________________________________________________________________________
Output_2 (Dense) (None, 1) 101 Hidden2[0][0]
__________________________________________________________________________________________________
Output_3 (Dense) (None, 1) 101 Hidden2[0][0]
__________________________________________________________________________________________________
Output_4 (Dense) (None, 1) 101 Hidden2[0][0]
==================================================================================================
Total params: 10,904
Trainable params: 10,904
Non-trainable params: 0
__________________________________________________________________________________________________
Assertions error:
AssertionError: in user code:
C:\Users\jules\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:806 train_function *
return step_function(self, iterator)
C:\Users\jules\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:796 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
C:\Users\jules\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1211 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
C:\Users\jules\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2585 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
C:\Users\jules\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2945 _call_for_each_replica
return fn(*args, **kwargs)
C:\Users\jules\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:789 run_step **
outputs = model.train_step(data)
C:\Users\jules\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:747 train_step
y_pred = self(x, training=True)
C:\Users\jules\anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:985 __call__
outputs = call_fn(inputs, *args, **kwargs)
C:\Users\jules\anaconda3\lib\site-packages\tensorflow\python\keras\engine\functional.py:385 call
return self._run_internal_graph(
C:\Users\jules\anaconda3\lib\site-packages\tensorflow\python\keras\engine\functional.py:517 _run_internal_graph
assert x_id in tensor_dict, 'Could not compute output ' + str(x)
AssertionError: Could not compute output Tensor("Output_1/BiasAdd_57:0", shape=(None, 1), dtype=float32)
I'm a beginner and I don't understand where the error come from because my inputs and outputs seems to hae the correct dimensions.
Previously, instead of using 3 inputs layers, I used one with a shape of 3:
Inputs = Input(shape=(3,),name="Inputs")
But this gave me terrible predictions (negative R2)

stacking LSTM layer on top of BERT encoder in Keras

I have been trying to stack a single LSTM layer on top of Bert embeddings, but whilst my model starts to train it fails on the last batch and throws the following error message:
Node: 'model/tf.reshape/Reshape'
Input to reshape is a tensor with 59136 values, but the requested shape has 98304
[[{{node model/tf.reshape/Reshape}}]] [Op:__inference_train_function_70500]
This is how I build the model and I honestly cannot figure out what is going wrong here:
batch_size = 128
bert_preprocess = hub.load('https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3')
bert_encoder = hub.KerasLayer('https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/4', trainable=True)
text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='text')
preprocessed_text = bert_preprocess(text_input)
outputs = bert_encoder(preprocessed_text) #shape=(None, 768)
bert_output = outputs['pooled_output']
l = tf.reshape(bert_output, [batch_size, 1, 768])
l = tf.keras.layers.LSTM(32, activation='relu')(l)
l = tf.keras.layers.Dropout(0.1, name='dropout')(l)
l = tf.keras.layers.Dense(8, activation='softmax', name="output")(l)
model = tf.keras.Model(inputs=[text_input], outputs = [l])
print(model.summary())
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=1, batch_size = batch_size)
this is the full output:
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
text (InputLayer) [(None,)] 0 []
keras_layer (KerasLayer) {'input_mask': (Non 0 ['text[0][0]']
e, 128),
'input_word_ids':
(None, 128),
'input_type_ids':
(None, 128)}
keras_layer_1 (KerasLayer) {'sequence_output': 109482241 ['keras_layer[0][0]',
(None, 128, 768), 'keras_layer[0][1]',
'default': (None, 'keras_layer[0][2]']
768),
'encoder_outputs':
[(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768),
(None, 128, 768)],
'pooled_output': (
None, 768)}
tf.reshape (TFOpLambda) (128, 1, 768) 0 ['keras_layer_1[0][13]']
lstm (LSTM) (128, 32) 102528 ['tf.reshape[0][0]']
dropout (Dropout) (128, 32) 0 ['lstm[0][0]']
output (Dense) (128, 8) 264 ['dropout[0][0]']
==================================================================================================
Total params: 109,585,033
Trainable params: 102,792
Non-trainable params: 109,482,241
__________________________________________________________________________________________________
None
WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x7fc4ff809440> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: 'arguments' object has no attribute 'posonlyargs'
To silence this warning, decorate the function with #tf.autograph.experimental.do_not_convert
WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x7fc4ff809440> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: 'arguments' object has no attribute 'posonlyargs'
To silence this warning, decorate the function with #tf.autograph.experimental.do_not_convert
18/19 [===========================>..] - ETA: 25s - loss: 1.5747 - accuracy: 0.5456Traceback (most recent call last):
File "bert-test-lstm.py", line 62, in <module>
model.fit(x_train, y_train, epochs=1, batch_size = batch_size)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/tensorflow/python/eager/execute.py", line 55, in quick_execute
inputs, attrs, num_outputs)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:
Detected at node 'model/tf.reshape/Reshape' defined at (most recent call last):
File "bert-test-lstm.py", line 62, in <module>
model.fit(x_train, y_train, epochs=1, batch_size = batch_size)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/training.py", line 1384, in fit
tmp_logs = self.train_function(iterator)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/training.py", line 1021, in train_function
return step_function(self, iterator)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/training.py", line 1010, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/training.py", line 1000, in run_step
outputs = model.train_step(data)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/training.py", line 859, in train_step
y_pred = self(x, training=True)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/base_layer.py", line 1096, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/functional.py", line 452, in call
inputs, training=training, mask=mask)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/functional.py", line 589, in _run_internal_graph
outputs = node.layer(*args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/engine/base_layer.py", line 1096, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/layers/core/tf_op_layer.py", line 226, in _call_wrapper
return self._call_wrapper(*args, **kwargs)
File "/Users/user/opt/anaconda3/envs/project/lib/python3.7/site-packages/keras/layers/core/tf_op_layer.py", line 261, in _call_wrapper
result = self.function(*args, **kwargs)
Node: 'model/tf.reshape/Reshape'
Input to reshape is a tensor with 59136 values, but the requested shape has 98304
The code runs perfectly fine if I just drop the LSTM and reshape layers - any help is appreciated.

You should use tf.keras.layers.Reshape in order to reshape bert_output into a 3D tensor and automatically taking into account the batch dimension.
Simply changing:
l = tf.reshape(bert_output, [batch_size, 1, 768])
into:
l = tf.keras.layers.Reshape((1,768))(bert_output)
should work.

CLDNN Compile and Training Issue

I am running into an issue running a CLDNN model in Python 3. I ran model.summary() and I did not see an issue. I am receiving a sizing/shaping error between Keras and Tensorflow.
Below is the code with the output error. Any input would be greatly apricated.
Update: reformatted the code and supplied the output as text.
def cldnn():
model = Sequential()
#input layer
model.add(keras.layers.InputLayer(input_shape=(2, 128, 1)))
#first layer
model.add(Conv2D(256, (1, 3), activation='relu', use_bias=True))
model.add(Dropout(0.2))
#second layer
model.add(Conv2D(256, (2, 3), activation='relu', use_bias=True))
#third layer
model.add(Conv2D(80, (1, 3), activation='relu', use_bias=True))
model.add(Dropout(0.2))
#fourth layer
model.add(Conv2D(80, (1, 3), activation='relu', use_bias=True))
#reshape data
model.add(Reshape((120,80)))
#fifth layer
model.add(LSTM(50, activation='tanh', return_sequences=True))
#sixth layer
model.add(Dense(128,
activation='relu',
use_bias=True,
kernel_initializer='normal'))
#output layer
model.add(Dense(10, activation='softmax', use_bias=True))
model.summary()
return model
#Create instance of CNN model graph
cldnn = cldnn()
#Compile model using an appropriate loss and optimizer algorithm
cldnn.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
cldnn_mdata = cldnn.fit(data_train, labels_train,
validation_data=(data_test, labels_test),
epochs=15,
batch_size=1024,
shuffle=True)
#Print accuracy of model on testing set after training
scores = cldnn.evaluate(data_test, labels_test)
print("Accuracy: %.2f%%" %(scores[1]*100))
#OUTPUT
Model: "sequential_4"
_________________________________________________________________
Layer (type) Output Shape Param # =================================================================
conv2d_16 (Conv2D) (None, 2, 126, 256) 1024
_________________________________________________________________
dropout_8 (Dropout) (None, 2, 126, 256) 0
_________________________________________________________________
conv2d_17 (Conv2D) (None, 1, 124, 256) 393472
_________________________________________________________________
conv2d_18 (Conv2D) (None, 1, 122, 80) 61520
_________________________________________________________________
dropout_9 (Dropout) (None, 1, 122, 80) 0
_________________________________________________________________
conv2d_19 (Conv2D) (None, 1, 120, 80) 19280
_________________________________________________________________
reshape_3 (Reshape) (None, 120, 80) 0
_________________________________________________________________
lstm_3 (LSTM) (None, 120, 50) 26200
_________________________________________________________________
dense_6 (Dense) (None, 120, 128) 6528
_________________________________________________________________
dense_7 (Dense) (None, 120, 10) 1290
=================================================================
Total params: 509,314
Trainable params: 509,314
Non-trainable params: 0
_________________________________________________________________
Epoch 1/15
WARNING:tensorflow:Model was constructed with shape (None, 2, 128, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 2, 128, 1), dtype=tf.float32, name='input_5'), name='input_5', description="created by layer 'input_5'"), but it was called on an input with incompatible shape (None, 28, 28, 1).
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-9-0e0ee3a37c65> in <module>()
52 epochs=15,
53 batch_size=1024,
---> 54 shuffle=True)
55
56 #Print accuracy of model on testing set after training
9 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
992 except Exception as e: # pylint:disable=broad-except
993 if hasattr(e, "ag_error_metadata"):
--> 994 raise e.ag_error_metadata.to_exception(e)
995 else:
996 raise
ValueError: in user code:
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:853 train_function *
return step_function(self, iterator)
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:842 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1286 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:2849 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3632 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:835 run_step **
outputs = model.train_step(data)
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:787 train_step
y_pred = self(x, training=True)
/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py:1037 __call__
outputs = call_fn(inputs, *args, **kwargs)
/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:369 call
return super(Sequential, self).call(inputs, training=training, mask=mask)
/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:415 call
inputs, training=training, mask=mask)
/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:550 _run_internal_graph
outputs = node.layer(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py:1037 __call__
outputs = call_fn(inputs, *args, **kwargs)
/usr/local/lib/python3.7/dist-packages/keras/layers/core.py:539 call
result.set_shape(self.compute_output_shape(inputs.shape))
/usr/local/lib/python3.7/dist-packages/keras/layers/core.py:530 compute_output_shape
self.target_shape)
/usr/local/lib/python3.7/dist-packages/keras/layers/core.py:518 _fix_unknown_dimension
raise ValueError(msg)
ValueError: total size of new array must be unchanged, input_shape = [27, 20, 80], output_shape = [120, 80]

AssertionError: Could not compute output Tensor("dense_17/Sigmoid:0", shape=(None, 1), dtype=float32)

I'm trying to train DC-CNN model for text classification on a given dataset.
What am I doing wrong here?
Code for Model:
def define_model(length, vocab_size):
# channel 1
inputs1 = Input(shape=(length,))
embedding1 = Embedding(vocab_size, 100)(inputs1)
conv1 = Conv1D(filters=32, kernel_size=4, activation='relu')(embedding1)
drop1 = Dropout(0.5)(conv1)
pool1 = MaxPooling1D(pool_size=1)(drop1)
flat1 = Flatten()(pool1)
# channel 2
inputs2 = Input(shape=(length,))
embedding2 = Embedding(vocab_size, 100)(inputs2)
conv2 = Conv1D(filters=32, kernel_size=6, activation='relu')(embedding2)
drop2 = Dropout(0.5)(conv2)
pool2 = MaxPooling1D(pool_size=1)(drop2)
flat2 = Flatten()(pool2)
merged = concatenate([flat1, flat2])
# interpretation
dense1 = Dense(10, activation='relu')(merged)
outputs = Dense(1, activation='sigmoid')(dense1)
model = Model(inputs=[inputs1, inputs2], outputs=outputs)
# compile
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# summarize
print(model.summary())
return model
model = define_model(length, vocab_size)
model.fit([trainX], array(trainLabels), epochs=10, batch_size=16)
I am getting this error:
AssertionError: in user code:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:806 train_function *
return step_function(self, iterator)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:796 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:789 run_step **
outputs = model.train_step(data)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:747 train_step
y_pred = self(x, training=True)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:985 __call__
outputs = call_fn(inputs, *args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py:386 call
inputs, training=training, mask=mask)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py:517 _run_internal_graph
assert x_id in tensor_dict, 'Could not compute output ' + str(x)
AssertionError: Could not compute output Tensor("dense_17/Sigmoid:0", shape=(None, 1), dtype=float32)
I have tried to reshape the inputs "trainX" and "trainLabels" by using this code but I got the same error
trainX=np.reshape(trainX,(40, 50))
trainLabels=np.reshape(trainLabels,(40, 1))
This is the summary of the model :
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_17 (InputLayer) [(None, 20)] 0
__________________________________________________________________________________________________
input_18 (InputLayer) [(None, 20)] 0
__________________________________________________________________________________________________
embedding_16 (Embedding) (None, 20, 100) 541100 input_17[0][0]
__________________________________________________________________________________________________
embedding_17 (Embedding) (None, 20, 100) 541100 input_18[0][0]
__________________________________________________________________________________________________
conv1d_16 (Conv1D) (None, 17, 32) 12832 embedding_16[0][0]
__________________________________________________________________________________________________
conv1d_17 (Conv1D) (None, 15, 32) 19232 embedding_17[0][0]
__________________________________________________________________________________________________
dropout_16 (Dropout) (None, 17, 32) 0 conv1d_16[0][0]
__________________________________________________________________________________________________
dropout_17 (Dropout) (None, 15, 32) 0 conv1d_17[0][0]
__________________________________________________________________________________________________
max_pooling1d_16 (MaxPooling1D) (None, 17, 32) 0 dropout_16[0][0]
__________________________________________________________________________________________________
max_pooling1d_17 (MaxPooling1D) (None, 15, 32) 0 dropout_17[0][0]
__________________________________________________________________________________________________
flatten_16 (Flatten) (None, 544) 0 max_pooling1d_16[0][0]
__________________________________________________________________________________________________
flatten_17 (Flatten) (None, 480) 0 max_pooling1d_17[0][0]
__________________________________________________________________________________________________
concatenate_8 (Concatenate) (None, 1024) 0 flatten_16[0][0]
flatten_17[0][0]
__________________________________________________________________________________________________
dense_16 (Dense) (None, 10) 10250 concatenate_8[0][0]
__________________________________________________________________________________________________
dense_17 (Dense) (None, 1) 11 dense_16[0][0]
==================================================================================================
Total params: 1,124,525
Trainable params: 1,124,525
Non-trainable params: 0
How can I fix this error Please?

since you have 2 inputs in keras model, so you have to split your trainX in to 2 different arrays, or a tuple of 2 arrays. you cannot give single array as input.