osm_inputs = Input(shape=(osm_max_points, osm_seq_len))
osm_model = LSTM(osm_max_points * 2, activation='relu')(osm_inputs)
concat = concatenate([brt_model, osm_model])
model = Reshape((1, concat.shape[-1].value))(concat)
for layer in range(REPEAT_HIDDEN):
model = LSTM(LSTM_UNITS, return_sequences=True)(model)
model = LeakyReLU()(model)
model = Dense(DENSE_UNITS, activation='relu')(model)
model = LSTM(LSTM_UNITS, activation='relu')(model) # Flatten
model = Dense(2)(model)
model = Model(inputs=[brt_inputs, osm_inputs], outputs=model)
model.compile(loss=univariate_gaussian_loss, optimizer=OPTIMIZER)
model.summary()
callbacks = [
TensorBoard(log_dir='./tensorboard_log/' + SIGNATURE, write_graph=False),
EpochLogger(input_slice=lambda x: x[0:2], target_slice=lambda x: x[2:3], stdout=True),
EarlyStopping(patience=40, min_delta=0.001)
]
history = model.fit(
x=[brt_vectors, osm_vectors],
y=surface_area_vectors,
epochs=EPOCHS,
batch_size=BATCH_SIZE,
validation_split=TRAIN_VALIDATE_SPLIT,
callbacks=callbacks).history
activation='relu',
padding="same",
kernel_initializer="he_normal"))(model)
model = (UpSampling2D((2, 2)))(model)
model = Conv2D(8,
kernel_size=(3, 3),
activation='relu',
padding="same",
kernel_initializer="he_normal")(model)
model = (UpSampling2D((2, 2)))(model)
model = Conv2D(1, (3, 3), activation="sigmoid", padding="same")(model)
model = Model(input_image, model)
model.compile(optimizer=Adam(lr=0.0001), loss='mse')
Encoder = Model(input_image, encoder)
## we will have Vto train in batches because the whole thing won't fit in memory
datagen = ImageDataGenerator(featurewise_center=True,
rescale=1. / 255,
validation_split=0.05)
training = datagen.flow_from_directory(
"sampled_tiles/training_junk_clusters_removed/",
class_mode="input",
batch_size=8,
target_size=(256, 256),
color_mode="grayscale")
history = model.fit_generator(training, epochs=20)
with open("models/model_save_10_25_256px_1024_56k_subset.json", 'w') as out:
