Exemple #1
0
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
Exemple #2
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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)