def train_and_predict(train_df, test_df):
# Data Cleaning
# clean the data
cleaner = DataCleaner()
cleaner.columns_with_no_nan(train_df)
cleaner.columns_with_no_nan(test_df)
train_df = cleaner.drop_columns(train_df)
train_df = cleaner.resolve_nan(train_df)
test_df = cleaner.drop_columns(test_df)
test_df = cleaner.resolve_nan(test_df)
# features engineering
train_df, test_df = engineer_features(train_df, test_df)
# train the model from Model
model = Classifier()
model = model.model()
# LabelEncoding/OneHotEncoding?
train_df = model.encode(train_df)
test_df = model.encode(test_df)
# training progress and results
model = model.train(model, train_df)
# predict on test_df with predict method from Model
y_test = model.predict(model, test_df)
return y_test
def test_coupled_weights_of_backbone():
"""
This function will fail because there are multiple models defined
in the keras/tensorflow graph which are not used during training.
Returns:
bool -- [description]
"""
data = mnist_data()
backbone = ResNet()
preds = backbone(data.get_test()[0])
gen = ResGen(backbone)
input_shape = gen.get_input_shape()
rand_data_shape = ((50, ) + input_shape[1:] + (1, ))
random_noise_data = np.random.normal(size=rand_data_shape)
discriminator = Discriminator(backbone)
classifier = Classifier(backbone, 10)
discriminator_predicitons_1 = discriminator(data.get_test()[0])
classifier_predicitons_1 = classifier.predict(data.get_test()[0])
generator_predictions_1 = gen.predict(random_noise_data)[0]
classifier.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
classifier.summary()
# classifier.fit(x=x_train,y=y_train,batch_size=6000,epochs=1, validation_data=(x_vali,y_vali),callbacks=[checkpoint])
classifier.fit(x=data.get_n_samples(35)[0],
y=data.get_n_samples(35)[1],
batch_size=6000,
epochs=1,
validation_data=data.get_vali())
discriminator_predicitons_2 = discriminator(data.get_test()[0])
classifier_predicitons_2 = classifier.predict(data.get_test()[0])
generator_predictions_2 = gen.predict(random_noise_data)[0]
discriminator_diff = discriminator_predicitons_1 - discriminator_predicitons_2
classifier_diff = classifier_predicitons_1 - classifier_predicitons_2
generator_diff = generator_predicitons_1 - generator_predicitons_2
return True
def train_classifier_depricated(tpu=False):
scope = strategy.scope()
print("Number of accelerators: ", strategy.num_replicas_in_sync)
data = mnist_data()
backbone = ResNet()
discriminator = Discriminator(backbone)
classifier = Classifier(backbone, 10)
preds = classifier.predict(data.get_test()[0])
classifier.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
classifier.summary()
if (tpu):
classifier = convert_model_for_tpu(classifier)
checkpoint = keras.callbacks.ModelCheckpoint(
'./checkpoints/classifier/classifier_{epoch:.2f}.h5',
monitor='val_loss',
verbose=0,
save_best_only=False,
save_weights_only=True)
# classifier.fit(x=x_train,y=y_train,batch_size=6000,epochs=1, validation_data=(x_vali,y_vali),callbacks=[checkpoint])
classifier.fit(x=data.get_n_samples(35)[0],
y=data.get_n_samples(35)[1],
batch_size=6000,
epochs=20,
validation_data=data.get_vali(),
callbacks=[checkpoint])
# import pdb; pdb.set_trace() # breakpoint 396fe169 //
backbone = classifier.get_backbone()
backbone.save_weights('backbone_weights.h5')
return (classifier, x_test)
