model.py

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
# more info on callbakcs: https://keras.io/callbacks/ model saver is cool too.
from tensorflow.keras.callbacks import TensorBoard
from keras.utils import to_categorical
import pickle
import time

pickle_in = open("X.pickle","rb")
X = pickle.load(pickle_in)

pickle_in = open("y.pickle","rb")
y = pickle.load(pickle_in)

X = X/255.0
y_binary = to_categorical(y)

dense_layers = [0, 1, 2]
layer_sizes = [32, 64, 128]
conv_layers = [1, 2, 3]

for dense_layer in dense_layers:
    for layer_size in layer_sizes:
        for conv_layer in conv_layers:
            NAME = "{}-conv-{}-nodes-{}-dense-{}".format(conv_layer, layer_size, dense_layer, int(time.time()))
            print(NAME)

            model = Sequential()

            model.add(Conv2D(layer_size, (3, 3), input_shape=X.shape[1:]))
            model.add(Activation('relu'))
            model.add(MaxPooling2D(pool_size=(2, 2)))

            for l in range(conv_layer-1):
                model.add(Conv2D(layer_size, (3, 3)))
                model.add(Activation('relu'))
                model.add(MaxPooling2D(pool_size=(2, 2)))

            model.add(Flatten())

            for _ in range(dense_layer):
                model.add(Dense(layer_size))
                model.add(Activation('relu'))

            model.add(Dense(3))
            model.add(Activation('softmax'))

            tensorboard = TensorBoard(log_dir="logs1/{}".format(NAME))

            model.compile(loss='categorical_crossentropy',
                          optimizer='adam',
                          metrics=['accuracy'],
                          )

            model.fit(X, y_binary,
                      batch_size=4,
                      epochs=10,
                      validation_split=0.2,
                      callbacks=[tensorboard])