Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
This pull request enhances the alexnet.ipynb notebook by aligning its architecture with the standard AlexNet model. The following changes have been made:
Added conv4 and conv5 layers to extend the convolutional section, including pooling, normalization, and dropout for each.
Updated the weights and biases dictionaries to include parameters for the new layers.
Adjusted the input to the fully connected layers to match the output of the added conv5 layer.
This pull request enhances the
alexnet.ipynbnotebook by aligning its architecture with the standard AlexNet model. The following changes have been made:conv4andconv5layers to extend the convolutional section, including pooling, normalization, and dropout for each.weightsandbiasesdictionaries to include parameters for the new layers.conv5layer.