In the Kaggle Dog Breed Identification kernel:
The bottleneck features from the VGG16 model (using Keras) are feed into a multinomial logistic regression in order to achieve a validation accuracy of 91.8%. How would I make the equivalent Neural Network to this multinomial logistic regression. I have tried the following:
model = Sequential() model.add(Dense(120,input_shape=train_data.shape[1:],use_bias=False)) model.add(Dense(120, activation='softmax',use_bias=False)) model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) early_stopping = EarlyStopping(monitor='val_loss', patience=2) model.fit(train_data, train_labels, epochs=50, batch_size=batch_size, validation_data=(validation_data, validation_labels), callbacks=[early_stopping])
How do I need to change the network architecture to perfectly mimic a multinomial logistic regression?