class TestTrainModel(TestCase):
def setUp(self):
self.config = Config()
self.test_train_model = TrainModel(self.config)
self.dummy_data_df = pd.DataFrame(
np.array([['val11', 'val12', 'val13'], ['val21', 'val22', 'val23'],
['val31', 'val32', 'val33']]),
columns=self.config.ALL_PRODUCT_ATTRS)
@mock.patch.object(Recommender, 'fit')
def test_train(self, mocked_fit):
# mock calls.
self.test_train_model.read_data = MagicMock(
return_value=self.dummy_data_df)
self.test_train_model.write_data = MagicMock(return_value=None)
self.test_train_model.transform_data = MagicMock(
return_value=(([1, 2]), ([3, 4]), ([5, 6]), ([7, 8])))
mocked_fit.return_value = (self.dummy_data_df, self.dummy_data_df)
# call the method to be tested.
self.test_train_model.train()
# assertions to make sure all methods are called with expected arguments.
self.test_train_model.read_data.assert_has_calls([
call('dummy_path1'),
call('dummy_path2'),
call('dummy_path3'),
call('dummy_path4')
])
self.test_train_model.transform_data.assert_has_calls([
call(self.dummy_data_df, self.config.ALL_PRODUCT_ATTRS, [
'SID_IDX', 'CONFIG_ID', 'PRODUCT_CATEGORY', 'PRODUCT_TYPE',
'BRAND'
], 'SID_IDX'),
call(self.dummy_data_df, self.config.ALL_PRODUCT_ATTRS, [
'CUSTOMER_IDX', 'CONFIG_ID', 'PRODUCT_CATEGORY',
'PRODUCT_TYPE', 'BRAND'
], 'CUSTOMER_IDX')
])
mocked_fit.assert_called_once_with(
([1, 2]), ([1, 2]), ([3, 4]), ([3, 4]), ([5, 6]), ([5, 6]),
([7, 8]), ([7, 8]), self.dummy_data_df, self.dummy_data_df)
self.test_train_model.write_data.assert_called_once_with(
self.dummy_data_df)
#!/usr/bin/env python3
import numpy as np
import tensorflow as tf
from train_model import TrainModel
from utils import load_images, load_csv, generate_triplets
images, filenames = load_images('HBTNaligned', as_array=True)
images = images.astype('float32') / 255
triplet_names = load_csv('FVTriplets.csv')
A, P, N = generate_triplets(images, filenames, triplet_names)
triplets = [A[:-2], P[:-2], N[:-2]] # to make all batches divisible by 32
tm = TrainModel('models/face_verification.h5', 0.2)
history = tm.train(triplets, epochs=1)
print(history.history)
output_dim=output_emb,
input_length=seq_len)(inputs)
forward_pass = tf.keras.layers.LSTM(rnn_unit,
return_sequences=True)(embedding)
forward_pass = tf.keras.layers.Dropout(dropout)(forward_pass)
forward_pass = tf.keras.layers.LSTM(rnn_unit)(forward_pass)
forward_pass = tf.keras.layers.Dense(dense_unit)(forward_pass)
forward_pass = tf.keras.layers.Dropout(dropout)(forward_pass)
outputs = tf.keras.layers.Dense(unique_notes + 1,
activation="softmax")(forward_pass)
model = tf.keras.Model(inputs=inputs,
outputs=outputs,
name='generate_scores_rnn')
return model
model = create_model(seq_len, unique_notes)
model.summary()
optimizer = tf.keras.optimizers.RMSprop()
loss_fn = tf.keras.losses.categorical_crossentropy
train_class = TrainModel(EPOCHS, note_tokenizer, sampled_200_midi,
FRAME_PER_SECOND, BATCH_NNET_SIZE, BATCH_SONG,
optimizer, loss_fn, TOTAL_SONGS, model, seq_len)
train_class.train()
model.save('model_ep4.h5')
pickle.dump(note_tokenizer, open("tokenizer.p", "wb"))
#!/usr/bin/env python3
import os
import numpy as np
import tensorflow as tf
from train_model import TrainModel
from utils import load_images, load_csv, generate_triplets
images, filenames = load_images('HBTNaligned', as_array=True)
triplet_names = load_csv('FVTriplets.csv')
A, P, N = generate_triplets(images, filenames, triplet_names)
triplets = [A[:-2], P[:-2], N[:-2]]
tm = TrainModel('models/face_verification.h5', 0.2)
tm.train(triplets, epochs=1)
base_model = tm.save('models/trained_fv.h5')
print(base_model is tm.base_model)
print(os.listdir('models'))