def test_seq_to_seq(self):
#print (self.get_random_states())
train_x = []
# Data size: 10 x (image + 2 actions) x board/action size
train_x = np.random.randint(0, 2, size=(10, 3, 9))
# train_x = [
# [
# [0.1, 1.0],
# [0.1, 1.0],
# [0.1, 1.0],
# [0.1, 1.0],
# [0.1, 1.0],
# ]]
# 1 being the batch size
# 10 being the length
#train_x = np.random.randint(low=0, high=2, size=(1, 10, 9))
train_y = [[0.11, 0.11, 0.11]] * 10
#train_y = [ 0.11 ]
train_y = np.array(train_y)
model = Sequential()
#model.add(layers.Flatten(input_shape=(3, 9))),
#model.add(layers.Embedding(input_shape=(10, 9), ))
model.add(
layers.LSTM(units=100, input_shape=(3, 9), return_sequences=True))
model.add(layers.Dropout(rate=0.25))
model.add(layers.Dense(50, activation='relu'))
model.add(layers.Dense(1, activation=None))
model.compile(optimizer='adam', loss=tf.losses.MSE, metrics=['mae'])
print(model.summary())
model.fit(x=train_x, y=train_y, epochs=100, verbose=0)
loss = model.evaluate(train_x, train_y, verbose=2)
self.assertLess(loss[0], 1e-04)
Lm1 = Sequential()
#q1. try without input?
Lm1.add(Input(shape=(784, )))
Lm1.add(Dense(num_classes, activation='softmax'))
#q2.try SparseCategoricalCrossentropy without one-hot
loss_object = tf.keras.losses.categorical_crossentropy
optimizer = tf.keras.optimizers.SGD(0.01)
#
train_loss = tf.keras.metrics.Mean(name='train_loss')
#try SparseCategoricalAccuracy
train_accuracy = tf.keras.metrics.CategoricalAccuracy(name='train_accuracy')
test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.CategoricalAccuracy(name='test_accuracy')
checkpoint_path = "./checkpoints/"
checkpoint_dir = os.path.dirname(checkpoint_path)
cp_callback = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,
verbose=1,
period=1)
#q3 metrics=xxx without []?
Lm1.compile(optimizer=optimizer, loss=loss_object, metrics=[train_accuracy])
#q4 train_ds?
Lm1.fit(train_ds, epochs=3, callbacks=[cp_callback])
loss, acc = Lm1.evaluate(train_ds)
print("saved model, loss: {:5.2f}, acc: {:5.2f}".format(loss, acc))
class FecModel(Model):
def __init__(self, loader):
self._loader = loader
self._num_train = 28709
self._num_val = 7178
self._batch_size = 64
self._num_epoch = 1
self.create_model()
def create_model(self):
self._model = Sequential()
self._model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(48, 48, 1)))
self._model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
self._model.add(MaxPooling2D(pool_size=(2, 2)))
self._model.add(Dropout(0.25))
self._model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
self._model.add(MaxPooling2D(pool_size=(2, 2)))
self._model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
self._model.add(MaxPooling2D(pool_size=(2, 2)))
self._model.add(Dropout(0.25))
self._model.add(Flatten())
self._model.add(Dense(1024, activation='relu'))
self._model.add(Dropout(0.5))
self._model.add(Dense(7, activation='softmax'))
self._model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.0001, decay=1e-6),
metrics=['accuracy'])
def train_model(self):
print('Load train data...')
train_generator = self._loader.prepare_train_data()
print('Load validation data...')
validation_generator = self._loader.prepare_validation_data()
model_info = self._model.fit_generator(
train_generator,
steps_per_epoch=self._num_train // self._batch_size,
epochs=self._num_epoch,
validation_data=validation_generator,
validation_steps=self._num_val // self._batch_size)
def evaluate_model(self):
print('Load validation data...')
evaluate_generator = self._loader.prepare_validation_data()
model_info = self._model.evaluate(evaluate_generator,
steps=self._num_train // self._batch_size,
batch_size=self._batch_size,
epochs=self._num_epoch)
return model_info
def save_model(self):
print('Save model...')
self._model.save_weights(FecConfig.model_file_name)
def load_model(self):
print('Load model...')
self._model.load_weights(FecConfig.model_file_name)
def make_prediction(self, input_folder):
processed_images = self._loader.prepare_data(input_folder)
print('Predicting data...')
results = []
for processed_image in processed_images:
for face_image in processed_image.face_images:
result = self._model.predict(face_image)
results.append(result)
print('Save results...')
self._loader.save_data(processed_images, results)