Tuning and optimizing neural networks with the Keras-Tuner package: https://keras-team.github.io/keras-tuner/
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Text-based tutorial and sample code: https://pythonprogramming.net/keras-tuner-optimizing-neural-network-tutorial/
Starting model:
from tensorflow import keras
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Activation
model = keras.models.Sequential()
model.add(Conv2D(32, (3, 3), input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors
model.add(Dense(10))
model.add(Activation("softmax"))
model.compile(optimizer="adam",
loss="sparse_categorical_crossentropy",
metrics=["accuracy"])
model.fit(x_train, y_train, batch_size=64, epochs=1, validation_data = (x_test, y_test))
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