def test_build_predict_func(self, get_model):
"""Test the build of a model"""
new_session()
X_tr = np.ones((train_samples, input_dim))
model = get_model()
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
model_name = model.__class__.__name__
pred_func = KTB.build_predict_func(model)
tensors = [X_tr]
if model_name != 'Model':
tensors.append(1.)
res = pred_func(tensors)
assert len(res[0]) == len(X_tr)
if K.backend() == 'tensorflow':
K.clear_session()
print(self)
def test_build_predict_func(self, get_model):
"""Test the build of a model"""
new_session()
X_tr = np.ones((train_samples, input_dim))
model = get_model()
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
model_name = model.__class__.__name__
pred_func = KTB.build_predict_func(model)
tensors = [X_tr]
if model_name != 'Model':
tensors.append(1.)
res = pred_func(tensors)
assert len(res[0]) == len(X_tr)
if K.backend() == 'tensorflow':
K.clear_session()
print(self)
def test_deserialization(self):
model = sequential()
model.compile(optimizer='sgd', loss='categorical_crossentropy')
ser_mod = to_dict_w_opt(model)
custom_objects = {'test_loss': [1, 2]}
custom_objects = {k: serialize(custom_objects[k])
for k in custom_objects}
model_from_dict_w_opt(ser_mod, custom_objects=custom_objects)
if K.backend() == 'tensorflow':
K.clear_session()
print(self)
def test_predict(self, get_model):
"""Test to predict using the backend"""
data, data_val = make_data(train_samples, test_samples)
model = get_model()
model.compile(optimizer='sgd', loss='categorical_crossentropy')
expe = Experiment(model)
expe.fit([data], [data_val])
KTB.predict(expe.model_dict, [data['X']])
if K.backend() == 'tensorflow':
K.clear_session()
print(self)
def test_experiment_instance_utils(self, get_model):
model = get_model()
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
expe = Experiment(model)
expe.model_dict = model
expe.backend_name = 'another_backend'
expe.model_dict = model
assert expe.backend is not None
expe = Experiment()
print(self)
def test_deserialization(self):
new_session()
model = sequential()
model.compile(optimizer='sgd', loss='categorical_crossentropy')
ser_mod = to_dict_w_opt(model)
custom_objects = {'test_loss': [1, 2]}
custom_objects = {
k: serialize(custom_objects[k])
for k in custom_objects
}
model_from_dict_w_opt(ser_mod, custom_objects=custom_objects)
if K.backend() == 'tensorflow':
K.clear_session()
print(self)
def test_predict(self, get_model):
"""Test to predict using the backend"""
new_session()
data, data_val = make_data(train_samples, test_samples)
model = get_model()
model.compile(optimizer='sgd', loss='categorical_crossentropy')
expe = Experiment(model)
expe.fit([data], [data_val])
KTB.predict(expe.model_dict, [data['X']], False)
KTB.predict(expe.model_dict, [data['X']], True)
if K.backend() == 'tensorflow':
K.clear_session()
print(self)
def test_experiment_instance_utils(self, get_model):
new_session()
model = get_model()
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
expe = Experiment(model)
expe.model_dict = model
expe.backend_name = 'another_backend'
expe.model_dict = model
assert expe.backend is not None
expe = Experiment()
print(self)
def prepare_model(get_model, get_loss_metric, custom):
model = get_model
loss, metric = get_loss_metric
cust_objects = dict()
if isinstance(metric, six.string_types):
metrics = [metric]
else:
cust_objects[metric.__name__] = metric
metrics = [metric()]
if not isinstance(loss, six.string_types):
cust_objects['cat_cross'] = loss
if isinstance(loss, list):
cust_objects['cat_cross'] = loss[-1]
if model.__class__.__name__ == 'Model':
loss_replace = loss
if isinstance(loss, list):
loss_replace = loss[-1]
loss = dict()
loss['main_loss'] = loss_replace
if custom:
cust_objects['Dropout_cust'] = return_custom
if isinstance(loss, list):
loss[-1] = loss[-1]()
elif inspect.isfunction(loss):
loss = loss()
elif isinstance(loss, dict):
loss = {k: v() for k, v in loss.items()}
model.compile(loss=loss,
optimizer='rmsprop',
metrics=metrics,
custom_objects=cust_objects)
return model, metrics, cust_objects
def prepare_model(get_model, get_loss_metric, custom):
model = get_model
loss, metric = get_loss_metric
cust_objects = dict()
if isinstance(metric, six.string_types):
metrics = [metric]
else:
cust_objects[metric.__name__] = metric
metrics = [metric()]
if not isinstance(loss, six.string_types):
cust_objects['cat_cross'] = loss
if isinstance(loss, list):
cust_objects['cat_cross'] = loss[-1]
if model.__class__.__name__ == 'Model':
loss_replace = loss
if isinstance(loss, list):
loss_replace = loss[-1]
loss = dict()
loss['main_loss'] = loss_replace
if custom:
cust_objects['Dropout_cust'] = return_custom
if isinstance(loss, list):
loss[-1] = loss[-1]()
elif inspect.isfunction(loss):
loss = loss()
elif isinstance(loss, dict):
loss = {k: v() for k, v in loss.items()}
model.compile(loss=loss,
optimizer='rmsprop',
metrics=metrics,
custom_objects=cust_objects)
return model, metrics, cust_objects
def test_fit(self, get_model):
"Test the training of a serialized model"
data, data_val = make_data(train_samples, test_samples)
model = get_model()
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
model_dict = dict()
model_dict['model_arch'] = to_dict_w_opt(model)
res = KTB.train(copy.deepcopy(model_dict['model_arch']), [data],
[data_val], [])
res = KTB.fit(NAME, VERSION, model_dict, [data], 'test', [data_val],
[])
assert len(res) == 4
if K.backend() == 'tensorflow':
K.clear_session()
print(self)
def test_fit(self, get_model):
"Test the training of a serialized model"
new_session()
data, data_val = make_data(train_samples, test_samples)
model = get_model()
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
model_dict = dict()
model_dict['model_arch'] = to_dict_w_opt(model)
res = KTB.train(copy.deepcopy(model_dict['model_arch']), [data],
[data_val], [])
res = KTB.fit(NAME, VERSION, model_dict, [data], 'test', [data_val],
[])
assert len(res) == 4
if K.backend() == 'tensorflow':
K.clear_session()
print(self)