How to solve ValueError in model.predict()?

I am new in neural network problems. I have searched for couple of hours but could not understand what should I do to fix this issue! I'm working with nsl-kdd dataset for intrusion detection system with convolutional neural net.

I stuck with this problem : ValueError: Input 0 of layer dense_14 is incompatible with the layer: expected axis -1 of input shape to have value 3904 but received input with shape [None, 3712]

Shapes:

x_train (125973, 122)

y_train (125973, 5)

x_test (22544, 116)

y_test (22544,)

After reshape :

x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1)) #(125973, 122, 1)

x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1)) #(22544, 116, 1)

Model :

model = Sequential()
model.add(Convolution1D(64, 3, padding="same",activation="relu",input_shape = (x_train.shape[1], 1)))
model.add(MaxPooling1D(pool_size=(2)))
model.add(Flatten())
model.add(Dense(128, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(5, activation="softmax"))

Compile :

model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])
model.fit(x_train, Y_train, epochs = 5, batch_size = 32)

pred = model.predict(x_test)  #problem is occurring for this line
y_pred= np.argmax(pred, axis = 1)

model summary

Your x_test should have same dimensions as x_train.
x_train = (125973, 122, 1)

x_test = (22544, 116, 1) # the second parameter must match the train set

Code sample:

import tensorflow as tf
import pandas as pd 
import numpy as np
from tensorflow.keras.layers import *
from tensorflow.keras import *


x1 = np.random.uniform(100, size =(125973, 122,1))
x2 = np.random.uniform(100, size =(22544, 122, 1))
y1 = np.random.randint(100, size =(125973,5), dtype = np.int32)
y2 = np.random.randint(2, size =(22544, ), dtype = np.int32)

def create_model2():
    model = Sequential()
    model.add(Convolution1D(64, 3, padding="same",activation="relu",input_shape = (x1.shape[1], 1)))
    model.add(MaxPooling1D(pool_size=(2)))
    model.add(Flatten())
    model.add(Dense(128, activation="relu"))
    model.add(Dropout(0.5))
    model.add(Dense(5, activation="softmax"))
    
    model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])
    return model

model = create_model2()
tf.keras.utils.plot_model(model, 'my_first_model.png', show_shapes=True)

You model looks like this:

Now if use your test set to create your model keeping your dimension as (22544, 116, 1).
You get a model that looks this.
As the dimensions are different the expected input and output of each layers are different.

When you have appropriate test dimensions the output works as expected:

pred = model.predict(x2)
pred

Output:

array([[1., 0., 0., 0., 0.],
       [1., 0., 0., 0., 0.],
       [1., 0., 0., 0., 0.],
       ...,
       [1., 0., 0., 0., 0.],
       [1., 0., 0., 0., 0.],
       [1., 0., 0., 0., 0.]], dtype=float32)