def test_get_block():
"""Test get residual block."""
K.set_image_data_format('channels_last')
Resnet3DBuilder.build((224, 224, 224, 1), 2, 'bottleneck',
[2, 2, 2, 2], reg_factor=1e-4)
assert True
with pytest.raises(ValueError):
Resnet3DBuilder.build((224, 224, 224, 1), 2, 'nullblock',
[2, 2, 2, 2], reg_factor=1e-4)
## convert to 1 + 4D space (1st argument represents number of rows in the dataset)
X_train = X_train.reshape(X_train.shape[0], 16, 16, 16, 1)
X_test = X_test.reshape(X_test.shape[0], 16, 16, 16, 1)
Y_train = to_categorical(Y_train, 10)
Y_test = to_categorical(Y_test, 10)
print("number of training examples = " + str(X_train.shape[0]))
print("number of test examples = " + str(X_test.shape[0]))
print("X_train shape: " + str(X_train.shape))
print("Y_train shape: " + str(Y_train.shape))
print("X_test shape: " + str(X_test.shape))
print("Y_test shape: " + str(Y_test.shape))
# model = Resnet3DBuilder.build_resnet_18((16, 16, 16, 1), 10)
model = Resnet3DBuilder.build((16, 16, 16, 1),
10,
basic_block, [1, 1, 1, 1],
reg_factor=1e-4)
adam = Adam(lr=0.0001)
model.compile(optimizer=adam,
loss='categorical_crossentropy',
metrics=['accuracy'])
earlystop = EarlyStopping(monitor='val_acc',
min_delta=0.001,
patience=5,
verbose=2,
restore_best_weights=True)
callback_list = [earlystop]
model_info = model.fit(X_train,
Y_train,
epochs=100,
