def main():
dataset = MnistDataSet(validation_sample_count=5000)
dataset.load_dataset()
train_program_path = UtilityFuncs.get_absolute_path(
script_file=__file__, relative_path="train_program.json")
train_program = TrainProgram(program_file=train_program_path)
cnn_lenet = TreeNetwork(run_id=0,
dataset=dataset,
parameter_file=None,
tree_degree=2,
tree_type=TreeType.hard,
problem_type=ProblemType.classification,
train_program=train_program,
list_of_node_builder_functions=[baseline_network])
optimizer = SgdOptimizer(network=cnn_lenet,
use_biased_gradient_estimates=True)
cnn_lenet.set_optimizer(optimizer=optimizer)
cnn_lenet.build_network()
cnn_lenet.init_session()
cnn_lenet.train()
dif = x_0 - x_1
nz = np.flatnonzero(dif)
if len(nz) != 0:
raise Exception("!!!ERROR!!!")
print("Correct Result.")
k = 3
D = MnistDataSet.MNIST_SIZE * MnistDataSet.MNIST_SIZE
threshold = 0.3
feature_count = 32
epsilon = 0.000001
batch_size = 100
dataset = MnistDataSet(validation_sample_count=5000)
dataset.load_dataset()
samples, labels, indices_list = dataset.get_next_batch()
index_list = np.arange(0, batch_size)
initializer = tf.contrib.layers.xavier_initializer()
x = tf.placeholder(tf.float32, name="x")
indices = tf.placeholder(tf.int64, name="indices")
# Convolution
x_image = tf.reshape(x, [-1, MnistDataSet.MNIST_SIZE, MnistDataSet.MNIST_SIZE, 1])
C = tf.get_variable(name="C", shape=[5, 5, 1, feature_count], initializer=initializer,
dtype=tf.float32)
b_c = tf.get_variable(name="b_c", shape=(feature_count,), initializer=initializer, dtype=tf.float32)
conv_without_bias = tf.nn.conv2d(x_image, C, strides=[1, 1, 1, 1], padding="SAME")
conv = conv_without_bias + b_c
# Branching
flat_x = tf.reshape(x, [-1, D])