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():
with pytest.raises(ValueError):
Model(force_cpu="test")
with pytest.raises(ValueError):
Model(training_device="test")
with pytest.raises(ValueError):
Model(random_state="test")
training_device = device("cpu")
Model(force_cpu=False, training_device=training_device, random_state=1969)
Model(force_cpu=False, training_device=None, random_state=1969)
Model(force_cpu=True, training_device=None, random_state=1969)
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",
)
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_load_for_inference_method():
model = Model()
model.set_model(pytorch_model)
model.compile(optimizer=Adam(), loss_function=MSELoss())
with pytest.raises(ValueError):
model.load_for_inference(123)
with pytest.raises(ValueError):
model.load_for_inference("")
model.load_for_inference("ignore/test.npy")
with pytest.raises(ValueError):
model = Model()
model.load_for_inference("ignore/test.npy")
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_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)
X_train = X[0:int(len(X)*train_data_size)]
y_train = y[0:int(len(y)*train_data_size)]
X_validation = X[int(len(X)*train_data_size):]
y_validation = y[int(len(y)*train_data_size):]
print("Size of Train data is", len(X_train))
print("Size of Validation data is ", len(X_validation))
print("Size of Test data is ", len(X_test))
# Making the model
# Initializing the PyTorch model
p_model = MNISTModel()
# Initializing a NeuralPy model
model = Model()
# Converting the PyTorch model to NeuralPy model
model.set_model(p_model)
# Printing the summary of the model
print(model.summary())
# Compiling the model
model.compile(optimizer=Adam(), loss_function=CrossEntropyLoss(), metrics=["accuracy"])
# Training the model
# Using the fit method
history = model.fit(train_data=(X_train, y_train), test_data=(X_validation, y_validation), epochs=10, batch_size=32)
# Evaluating the model