it says:

One of the dimensions in the output is <= 0 due to downsampling in conv1d_4. Consider increasing the input size. Received input shape [None, 9600, 1, 1] which would produce output shape with a zero or negative value in a dimension.

here’s what i did so far :

dataset_url=”https://github.com/Kaustav-coder/cnn/blob/main/cnn.csv”

from keras.models import Sequential
from keras.layers import Dense,Flatten
from keras.layers import Conv1D, MaxPooling1D, Dropout
from keras.layers import Embedding
from keras.preprocessing import sequence
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

data=pd.read_csv('cnn.csv')
y=data['voltage']
x=data.drop(['voltage'],axis=1)

label_encoder = preprocessing.LabelEncoder() 
y_enc = label_encoder.fit_transform(y)
x_reshaped = x.values.reshape(x.shape[0], x.shape[1],1)
x_train,x_test,y_train,y_test= train_test_split(x_reshaped,y_enc,test_size=0.2,random_state=42)

model = Sequential()
model.add(Conv1D(filters=64, kernel_size=10, activation='relu', input_shape=(9600,1,1)))
model.add(MaxPooling1D(pool_size=1))
model.add(Conv1D(filters=64, kernel_size=10, activation='relu'))
model.add(MaxPooling1D(pool_size=1))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(128, activation='softmax'))