#TimeDistributed:
#(1). take Conv2D model and actually make it a "Model" according to the functional API
conv_model_single_image_as_model = Model(inputs=[image_input],
outputs=[conv_model_single_image])
#(2). make it time distributed or bidirectional(TimeDistributed)
conv_model_time_distributed = TimeDistributed(
conv_model_single_image_as_model)(image_inputs)
#(3). after TimeDistributed we have a tensor of shape (batch_size,number_of_time_steps,single_model_output)
# so i need to add a top layer to output something of desired shape:
conv_model_time_distributed = Flatten()(conv_model_time_distributed)
conv_model_time_distributed = Dense(2)(conv_model_time_distributed)
#(3). make the whole thing, after TimeDistributed, a Model according to the functional API:
#K.set_learning_phase(0)
conv_model_time_distributed = Model(inputs=[image_inputs],
outputs=[conv_model_time_distributed])
conv_model_time_distributed._uses_learning_phase = True #for learning=True, for testing = False
#Visualize Model:
if flag_plot_model == 1:
keras.utils.plot_model(conv_model_single_image_as_model)
keras.utils.vis_utils.plot_model(conv_model_single_image_as_model)
from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot
SVG(
model_to_dot(conv_model_single_image_as_model).create(prog='dot',
format='svg'))
#Summarize Model:
conv_model_single_image_as_model.summary()
conv_model_time_distributed.summary()
