def test_mse_get_layer_method_without_parameters(): x = MSELoss() details = x.get_loss_function() assert isinstance(details, dict) == True assert issubclass(details["loss_function"], _MSELoss) == True assert isinstance(details["keyword_arguments"], dict) == True assert details["keyword_arguments"]["reduction"] == 'mean'
def test_cce_get_layer_method(reduction):
x = MSELoss(reduction=reduction)
details = x.get_loss_function()
assert isinstance(details, dict) == True
assert issubclass(details["loss_function"], _MSELoss) == True
assert isinstance(details["keyword_arguments"], dict) == True
assert details["keyword_arguments"]["reduction"] == reduction
def test_model_set_model_method():
model = Model()
model.set_model(pytorch_model)
model.compile(optimizer=Adam(), loss_function=MSELoss())
with pytest.raises(ValueError):
model = Model()
model.set_model(None)
def test_model_summary_method():
model = Model()
model.set_model(pytorch_model)
model.compile(optimizer=Adam(), loss_function=MSELoss())
model.summary()
with pytest.raises(Exception):
model = Model()
model.summary()
def test_models_compile_method():
model = Model()
model.set_model(pytorch_model)
model.compile(optimizer=Adam(), loss_function=MSELoss())
with pytest.raises(ValueError):
model = Model()
model.set_model(pytorch_model)
model.compile(optimizer=Adam(),
loss_function=MSELoss(),
metrics=["test"])
with pytest.raises(ValueError):
model = Model()
model.set_model(pytorch_model)
model.compile(optimizer=Adam(),
loss_function=MSELoss(),
metrics="test")
def test_model_save_for_inference_method():
model = Model()
model.set_model(pytorch_model)
model.compile(optimizer=Adam(), loss_function=MSELoss())
with pytest.raises(ValueError):
model.save_for_inference(123)
with pytest.raises(ValueError):
model.save_for_inference("")
model.save_for_inference("ignore/test.npy")
def test_model_evaluate_method():
model = Model()
model.set_model(pytorch_model)
model.compile(optimizer=Adam(), loss_function=MSELoss())
TrainLogger("ignore/")
test_gen = train_generator()
model.fit(
train_data=(X_train, y_train),
validation_data=(X_validation, y_validation),
epochs=1,
batch_size=32,
)
model.evaluate(test_data=(X_test, y_test))
model.evaluate(test_data=(X_test, y_test), batch_size=4)
model.evaluate(test_data=test_gen, batch_size=4, tests_steps=4)
with pytest.raises(ValueError):
model.evaluate(test_data=(X_test, y_test), batch_size=400)
def test_is_valid_layer():
assert is_valid_layer(Dense(n_nodes=3)) == True
assert is_valid_layer(ReLU()) == True
assert is_valid_layer(MSELoss()) == False
def test_cce_should_throw_value_error(reduction):
with pytest.raises(ValueError) as ex:
x = MSELoss(reduction=reduction)
# Generating the data X_train = np.random.rand(100, 1) * 10 y_train = X_train + 5 *np.random.rand(100, 1) X_validation = np.random.rand(100, 1) * 10 y_validation = X_validation + 5 * np.random.rand(100, 1) X_test = np.random.rand(10, 1) * 10 y_test = X_test + 5 * np.random.rand(10, 1) # Making the model model = Sequential() model.add(Dense(n_nodes=1, n_inputs=1)) # Building the model model.build() # Compiling the model model.compile(optimizer=Adam(), loss_function=MSELoss()) # Printing model summary model.summary() # Training the model history = model.fit(train_data=(X_train, y_train), validation_data=(X_validation, y_validation), epochs=300, batch_size=4) # Predicting some values evaluated = model.evaluate(test_data=(X_test, y_test), batch_size=4) print(evaluated)
def test_model_fit_method():
model = Model()
model.set_model(pytorch_model)
model.compile(optimizer=Adam(), loss_function=MSELoss())
logger = TrainLogger("ignore/")
model.fit(
train_data=(X_train, y_train),
validation_data=(X_validation, y_validation),
epochs=1,
batch_size=32,
)
model.fit(
train_data=(X_train, y_train),
validation_data=(X_validation, y_validation),
epochs=1,
batch_size=32,
callbacks=[logger],
)
train_gen = train_generator()
validation_gen = train_generator()
model.fit(
train_data=train_gen,
validation_data=validation_gen,
epochs=1,
batch_size=4,
steps_per_epoch=5,
validation_steps=5,
)
model.fit(
train_data=train_gen,
validation_data=validation_gen,
epochs=1,
batch_size=4,
steps_per_epoch=5,
validation_steps=5,
callbacks=[logger],
)
with pytest.raises(ValueError):
model.fit(
train_data=(X_train, y_train),
validation_data=(X_validation, y_validation),
epochs=1,
batch_size=1024,
)
with pytest.raises(ValueError):
model.fit(
train_data=(X_train, y_train[:-1]),
validation_data=(X_validation, y_validation),
epochs=-20,
batch_size=1024,
)
with pytest.raises(ValueError):
model.fit(
train_data=(X_train, y_train[:-1]),
validation_data=(X_validation, y_validation),
epochs=1,
batch_size=-10,
)
with pytest.raises(ValueError):
model.fit(
train_data=(X_train, y_train[:-1]),
validation_data=(X_validation, y_validation),
epochs=1,
batch_size=32,
callbacks="test",
)
with pytest.raises(ValueError):
train_gen = train_generator()
validation_gen = train_generator()
model.fit(
train_data=train_gen,
validation_data=validation_gen,
epochs=1,
batch_size=32,
steps_per_epoch=-123,
validation_steps=5,
)
with pytest.raises(ValueError):
train_gen = train_generator()
validation_gen = train_generator()
model.fit(
train_data=train_gen,
validation_data=validation_gen,
epochs=1,
batch_size=32,
steps_per_epoch="test",
validation_steps=5,
)
with pytest.raises(ValueError):
train_gen = train_generator()
validation_gen = train_generator()
model.fit(
train_data=train_gen,
validation_data=validation_gen,
epochs=1,
batch_size=32,
steps_per_epoch=5,
validation_steps=-23,
)
with pytest.raises(ValueError):
train_gen = train_generator()
validation_gen = train_generator()
model.fit(
train_data=train_gen,
validation_data=validation_gen,
epochs=1,
batch_size=32,
steps_per_epoch=5,
validation_steps="asd",
)
''' model = Sequential() model.add(Dense(n_nodes=1, n_inputs=3)) model.add(ReLU()) model.add(Dense(n_nodes=2)) model.add(ReLU()) model.add(Dense(n_nodes=1)) model.add(ReLU()) # Building the Model model.build() # Compiling model.compile(optimizer=Adam(), loss_function=MSELoss(), metrics=["accuracy"]) print(model.summary()) # Data for XOR x_train = np.array([[0, 0, 1], [0, 1, 1], [1, 0, 1]]) x_test = np.array([[1, 1, 1]]) y_train = np.array([[0], [1], [1]], dtype=np.float32) y_test = np.array([[0]], dtype=np.float32) # Training the model model.fit(train_data=(x_train, y_train), epochs=20, batch_size=1) # Prediction print(model.predict(x_test[0]))