# for point_sequence in target_triangles]
(set_size, max_points, GEO_VECTOR_LEN) = np.array(target_vectors).shape
inputs = Input(shape=training_vectors.shape[1:])
model = RepeatVector(max_points)(inputs)
model = LSTM(LATENT_SIZE, activation='relu', return_sequences=True)(model)
model = Dense(32, activation='relu')(model)
model = LSTM(LATENT_SIZE, activation='relu', return_sequences=True)(model)
model = Dense(32, activation='relu')(model)
model = Dense(17)(model)
model = Model(inputs, model)
loss = GaussianMixtureLoss(
num_points=max_points,
num_components=COMPONENTS).geom_gaussian_mixture_loss
model.compile(loss=loss, optimizer=OPTIMIZER)
model.summary()
callbacks = [
TensorBoard(log_dir='./tensorboard_log/' + TIMESTAMP + ' ' + SCRIPT_NAME,
write_graph=False),
EpochLogger(input_func=lambda x: [
Polygon(np.reshape(x, (6, 2))[0:3]).wkt,
Polygon(np.reshape(x, (6, 2))[3:]).wkt
],
target_func=lambda x: [GeoVectorizer.decypher(x)],
predict_func=lambda x: [
Point(point).wkt for point in GeoVectorizer(
gmm_size=COMPONENTS).decypher_gmm_geom(x)
],
aggregate_func=lambda x: save_plot(
osm_inputs = Input(shape=(osm_max_points, OSM_INPUT_VECTOR_LEN))
osm_model = LSTM(osm_max_points * 2, activation='relu')(osm_inputs)
concat = concatenate([brt_model, osm_model])
model = RepeatVector(target_max_points)(concat)
for layer in range(REPEAT_HIDDEN):
model = LSTM(LSTM_SIZE, activation='relu', return_sequences=True)(model)
model = TimeDistributed(Dense(DENSE_SIZE, activation='relu'))(model)
model = Dense(output_seq_length)(model)
model = Model(inputs=[brt_inputs, osm_inputs], outputs=model)
model.compile(
loss=Loss.geom_gaussian_mixture_loss,
optimizer=OPTIMIZER)
model.summary()
# Callbacks
callbacks = [
TensorBoard(log_dir='./tensorboard_log/' + TIMESTAMP + ' ' + SCRIPT_NAME, write_graph=False),
DecypherAll(gmm_size=GAUSSIAN_MIXTURE_COMPONENTS,
plot_dir=PLOT_DIR,
input_slice=lambda x: x[:2],
target_slice=lambda x: x[2:3],
stdout=False),
EarlyStopping(patience=40, min_delta=1)
]
history = model.fit(
