model.add(Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu"))
model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))
model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))
model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu"))
model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))
model.add(Flatten())
input = layers.Input(batch_shape=(batch_size, time_step, INPUT_HEIGHT, INPUT_WIDTH, 1))
tdOut = td(model)(input)
lstmOut = layers.LSTM(50, activation='tanh')(tdOut)
preds = layers.Dense(5, activation='relu')(lstmOut)
tdmodel = tf.keras.models.Model(inputs=input, outputs=preds)
opt = tf.keras.optimizers.Adam(learning_rate=0.001)
tdmodel.compile(optimizer=opt, loss='MSE', metrics=['accuracy'])
training_generator = DataSequencer(trainPaths, trainLabels, batch_size, time_step, (INPUT_HEIGHT, INPUT_WIDTH, 1))
testing_generator = DataSequencer(testPaths, testLabels, batch_size, time_step, (INPUT_HEIGHT, INPUT_WIDTH, 1))
history = tdmodel.fit(x=training_generator,
epochs=epochs,
steps_per_epoch=len(training_generator),
base_model = tf.keras.applications.VGG16(weights='imagenet',
input_shape=(64, 64, 3),
include_top=False)
cnnOut = base_model.layers[18].output
cnnModel = tf.keras.Model(base_model.input, cnnOut)
for layer in cnnModel.layers:
layer.trainable = False
# or if we want to set the first 20 layers of the network to be non-trainable
for layer in cnnModel.layers[-8:]:
layer.trainable = True
input = layers.Input(batch_shape=(args.batch_size, args.time_step, 64, 64,
3))
tdOut = td(cnnModel)(input)
flOut = td(layers.Flatten())(tdOut)
lstmOut = layers.LSTM(50, activation='tanh')(flOut)
preds = layers.Dense(syn_head_gen.num_bins, activation='softmax')(lstmOut)
model = tf.keras.models.Model(inputs=input, outputs=preds)
for i, layer in enumerate(model.layers):
print(i, layer.name)
for layer in model.layers:
layer.trainable = False
# or if we want to set the first 20 layers of the network to be non-trainable
for layer in model.layers[-8:]:
layer.trainable = True
print(layer.name)
s_train_roll)
full_train_bg = b_train_bg + k_train_bg + s_train_bg
full_train_video_start = np.vstack(
(b_train_video_start, k_train_video_start, s_train_video_start))
test_images, test_pitch, test_yaw, test_roll, test_bg, test_video_start = \
syn_head_gen.create_data(args.biwi_test_dir, args.biwi_model_test_list, args.biwi_move_test_list)
image = cv2.imread(full_train_images[0, 0], 1)
model = tf.keras.Sequential()
model.add(
td(layers.Conv2D(filters=64,
kernel_size=(3, 3),
strides=4,
padding='same',
activation=tf.nn.relu),
input_shape=(args.time_step, 64, 64, 3)))
model.add(td(layers.MaxPool2D(pool_size=(3, 3), strides=2)))
model.add(
td(
layers.Conv2D(filters=192,
kernel_size=(3, 3),
padding='same',
activation=tf.nn.relu)))
model.add(td(layers.MaxPool2D(pool_size=(3, 3), strides=2)))
model.add(
td(
layers.Conv2D(filters=384,
kernel_size=(3, 3),
padding='same',