def run(model_id):
"""Run experiment."""
config = configs[model_id]
logger.info('\n\n\ntrain model {}'.format(model_id))
# prepare data
if config['preprocess_fn'] is not None:
function = getattr(data_generator, config['preprocess_fn'])
preprocess_fn = partial(function, **config['preprocess'])
else:
preprocess_fn = None
generator = Generator(path=PATH_TRAIN,
IDs=meta_train.index.tolist(),
labels=meta_train[['target']],
preprocessing_fn=preprocess_fn,
shuffle=False, batch_size=64,
**config['generator'])
X, y = generate_train_data(generator, meta_train)
logger.info('X shape: {}, y shape: {}'.format(X.shape, y.shape))
# define model
model_function = getattr(models, config['model_name'])
nn_model = partial(model_function,
input_shape=(X.shape[1:]),
**config['model_params'])
nn_model().summary(print_fn=logger.info)
model = KerasModel(nn_model, logger=logger, **config['train'])
# train and save model
cross_val = CrossValidation(X=X, y=y, Xtest=X[:100],
logger=logger, **config['cv'])
pred, pred_test, metrics, trained_models = cross_val.run_cv(model)
for i, model in enumerate(trained_models):
path = os.path.join(MODELS_PATH, 'model_{}_{}.h5'.format(model_id, i))
model.save(path)
print("train_x", train_x.shape, train_x.dtype)
input_shape = (img_width, img_height, img_num_channels)
logdir = "logs/" + datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)
model = KerasModel(input_shape)
# tf.keras.utils.plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)
model.compile(
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
optimizer=keras.optimizers.Adam(0.001),
metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
# Fit data to model
history = model.fit(train_x,
train_y,
callbacks=[tensorboard_callback],
batch_size=50,
epochs=6,
verbose=True,
validation_split=0.2)
model.summary()
score = model.evaluate(test_x, test_y, verbose=0)
model.save('saved_model/model')
probabilities = model.predict(test_x, verbose=True)
print(probabilities)