# keras.layers.Dense(64, activation='sigmoid'),
# keras.layers.Dense(32, activation='sigmoid'),
# keras.layers.Dense(7, activation='softmax')])
]
# Different model compilation options to try
optimizers = [keras.optimizers.SGD]
learning_rates = [.05, .001, .0005]
# Different training options to try
epochs = [10]
batch_size = [1, 16, 32, 64, 128, 256, 1024, 2048, 4096, 8192]
# Generate all combinations of the compilation options
compile_options = [{'optimizer': combo[0], 'learning_rate': combo[1]}
for combo in Test.generate_all_combinations([optimizers, learning_rates])]
# Generate all combinations of the training options
train_options = [{'epochs': combo[0], 'batch_size': combo[1]}
for combo in Test.generate_all_combinations([epochs, batch_size])]
results = pd.DataFrame(columns=['model', 'optimizer', 'learning_rate', 'epochs', 'batch_size', 'loss', 'accuracy'])
# For each model architecture
for i, model in enumerate(models):
print("Model " + str(i))
# For each combo of compilation options
for j, c in enumerate(compile_options):
print("Compiling option " + str(j))
