def test_count_of_network_parameters():
batch_size, width, height, rgb = 64, 14, 28, 1
units_per_layer = 240
hyper = {
'width_height': (width, height, rgb),
'model_type': 'GS',
'batch_size': batch_size,
'units_per_layer': units_per_layer,
'temp': tf.constant(0.1)
}
shape = (batch_size, width, height, rgb)
sop = SOP(hyper=hyper)
sop.build(input_shape=shape)
print('\nTEST: Number of parameters in the network')
assert sop.h1_dense.count_params() == (392 + 1) * units_per_layer
assert sop.h2_dense.count_params() == (units_per_layer +
1) * units_per_layer
assert sop.out_dense.count_params() == (units_per_layer + 1) * 392
from Utils.load_data import load_mnist_sop_data
from Models.SOP import SOP
from Models.SOPOptimizer import viz_reconstruction
import tensorflow as tf
model_type = 'GS'
hyper = {
'width_height': (14, 28, 1),
'model_type': model_type,
'batch_size': 64,
'learning_rate': 0.0003,
'epochs': 100,
'iter_per_epoch': 937,
'temp': tf.constant(0.67)
}
data = load_mnist_sop_data(batch_n=hyper['batch_size'], epochs=hyper['epochs'])
train, test = data
model = SOP(hyper=hyper)
results_file = './Log/model_weights_GS.h5'
shape = (hyper['batch_size'], ) + hyper['width_height']
shape = (64, 14, 28, 1)
model.build(input_shape=shape)
model.load_weights(filepath=results_file)
for x_test in test.take(10):
images = x_test
viz_reconstruction(test_image=images, model=model)