Fixing Keras ValueError: ndim=4 expected, ndim=5 found

├Źndice
  1. Overview
  2. The Error
  3. The Solution
  4. Conclusion

Overview

If you are using Keras and have encountered the error "ValueError: ndim=4 expected, ndim=5 found", don't worry, you are not alone. This error typically occurs when the input shape of the model is incorrect. In this article, we will discuss what this error means and how to fix it.

The Error

When you encounter the error "ValueError: ndim=4 expected, ndim=5 found", it means that the input shape of the model is incorrect. This error occurs when the input shape of the model is a 5-dimensional tensor instead of a 4-dimensional tensor.

The Solution

To fix this error, you need to change the input shape of the model to a 4-dimensional tensor. This can be done by adding a new dimension to the input data.

Here is an example code snippet that shows how to fix this error:


import numpy as np
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

# Create a model with an incorrect input shape
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(128, 128, 3, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))

# Create an input data with incorrect shape
x_train = np.random.rand(10, 128, 128, 3, 1)

# Evaluate the model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(x_train, y_train)

# Fix the input shape
x_train = np.random.rand(10, 128, 128, 3)
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(128, 128, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))

# Evaluate the model again
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(x_train, y_train)

In this example, we first create a model with an incorrect input shape. We then create an input data with the incorrect shape and evaluate the model, which results in the "ValueError: ndim=4 expected, ndim=5 found" error.

To fix the error, we add a new dimension to the input data and create a new model with the correct input shape. We then evaluate the model again, which should now run without any errors.

Conclusion

In summary, the "ValueError: ndim=4 expected, ndim=5 found" error in Keras occurs when the input shape of the model is incorrect. To fix this error, you need to change the input shape of the model to a 4-dimensional tensor. This can be done by adding a new dimension to the input data.

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