ValueError: cannot reshape array of size 3 into shape (1,80)

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We Are Going To Discuss About ValueError: cannot reshape array of size 3 into shape (1,80). So lets Start this Python Article.

Contents
  1. ValueError: cannot reshape array of size 3 into shape (1,80)
  2. Solution 1
  3. Conclusion

ValueError: cannot reshape array of size 3 into shape (1,80)

  1. How to solve ValueError: cannot reshape array of size 3 into shape (1,80)

    try the following with the two different values for n:
    import numpy as np n = 10160 #n = 10083 X = np.arange(n).reshape(1,-1) np.shape(X) X = X.reshape([X.shape[0], X.shape[1],1]) X_train_1 = X[:,0:10080,:] X_train_2 = X[:,10080:10160,:].reshape(1,80) np.shape(X_train_2)
    If you cannot make sure that X is 10160 long I suggest one of the following solutions:
    X_train_1 with 10080 samples, X_train_2 with the rest:
    X = X.reshape([X.shape[0], X.shape[1],1]) X_train_1 = X[:,0:10080,:] # X_train_1 with 10080 samples X_train_2 = X[:,10080:,:].reshape(1,-1) # X_train_2 with the remaining samples
    Or X_train_2 with 80 samples, X_train_1 with the rest:
    X = X.reshape([X.shape[0], X.shape[1],1]) X_train_1 = X[:,0:-80,:] # X_train_1 with the remaining samples X_train_2 = X[:,-80:,:].reshape(1,80) # X_train_2 with 80 samples

  2. ValueError: cannot reshape array of size 3 into shape (1,80)

    try the following with the two different values for n:
    import numpy as np n = 10160 #n = 10083 X = np.arange(n).reshape(1,-1) np.shape(X) X = X.reshape([X.shape[0], X.shape[1],1]) X_train_1 = X[:,0:10080,:] X_train_2 = X[:,10080:10160,:].reshape(1,80) np.shape(X_train_2)
    If you cannot make sure that X is 10160 long I suggest one of the following solutions:
    X_train_1 with 10080 samples, X_train_2 with the rest:
    X = X.reshape([X.shape[0], X.shape[1],1]) X_train_1 = X[:,0:10080,:] # X_train_1 with 10080 samples X_train_2 = X[:,10080:,:].reshape(1,-1) # X_train_2 with the remaining samples
    Or X_train_2 with 80 samples, X_train_1 with the rest:
    X = X.reshape([X.shape[0], X.shape[1],1]) X_train_1 = X[:,0:-80,:] # X_train_1 with the remaining samples X_train_2 = X[:,-80:,:].reshape(1,80) # X_train_2 with 80 samples

Solution 1

try the following with the two different values for n:

import numpy as np
n = 10160
#n = 10083
X = np.arange(n).reshape(1,-1)
np.shape(X)

X = X.reshape([X.shape[0], X.shape[1],1])
X_train_1 = X[:,0:10080,:]
X_train_2 = X[:,10080:10160,:].reshape(1,80)
np.shape(X_train_2)

If you cannot make sure that X is 10160 long I suggest one of the following solutions:

X_train_1 with 10080 samples, X_train_2 with the rest:

X = X.reshape([X.shape[0], X.shape[1],1])
X_train_1 = X[:,0:10080,:] # X_train_1 with 10080 samples
X_train_2 = X[:,10080:,:].reshape(1,-1) # X_train_2 with the remaining samples

Or X_train_2 with 80 samples, X_train_1 with the rest:

X = X.reshape([X.shape[0], X.shape[1],1])
X_train_1 = X[:,0:-80,:] # X_train_1 with the remaining samples
X_train_2 = X[:,-80:,:].reshape(1,80) # X_train_2 with 80 samples

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Conclusion

So This is all About This Tutorial. Hope This Tutorial Helped You. Thank You.

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I am an Information Technology Engineer. I have Completed my MCA And I have 4 Year Plus Experience, I am a web developer with knowledge of multiple back-end platforms Like PHP, Node.js, Python and frontend JavaScript frameworks Like Angular, React, and Vue.

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