plot_matrix_score
import torch
import torch.nn as nn
if __name__ == "__main__":
config = tf.ConfigProto()
config.gpu_options.allow_growth = True # dynamically grow the memory used on the GPU
sess = tf.Session(config=config)
set_session(sess)
single_class_ind = 1
(x_train, y_train), (x_val,
y_val), (x_test,
y_test) = load_hits(n_samples_by_class=10000,
test_size=0.20,
val_size=0.10,
return_val=True)
print(x_train.shape)
print(x_val.shape)
print(x_test.shape)
transformer = TransTransformer(8, 8)
n, k = (10, 4)
mdl = create_wide_residual_network(input_shape=x_train.shape[1:],
num_classes=transformer.n_transforms,
depth=n,
widen_factor=k)
mdl.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['acc'])
import tensorflow as tf
from tqdm import tqdm
from scripts.detached_transformer_od_hits import plot_histogram_disc_loss_acc_thr, \
dirichlet_normality_score, fixed_point_dirichlet_mle, calc_approx_alpha_sum
import matplotlib.pyplot as plt
if __name__ == "__main__":
config = tf.ConfigProto()
config.gpu_options.allow_growth = True # dynamically grow the memory used on the GPU
sess = tf.Session(config=config)
set_session(sess)
single_class_ind = 1
(x_train, y_train), (x_val, y_val), (x_test, y_test) = load_hits(n_samples_by_class=16000,
test_size=0.25,
val_size=0.125, return_val=True)
print(x_train.shape)
print(x_val.shape)
print(x_test.shape)
transformer = TransTransformer(8, 8)
# n, k = (10, 4)
#
# mdl = create_wide_residual_network(input_shape=x_train.shape[1:],
# num_classes=transformer.n_transforms,
# depth=n, widen_factor=k)
mdl = create_simple_network(input_shape=x_train.shape[1:],
num_classes=2, dropout_rate=0.5)
mdl.compile(optimizer='adam', loss='categorical_crossentropy',
if __name__ == "__main__":
config = tf.ConfigProto()
config.gpu_options.allow_growth = True # dynamically grow the memory used on the GPU
sess = tf.Session(config=config)
set_session(sess)
save_path = '../results/Transforms_hits'
check_paths(save_path)
single_class_ind = 1
(x_train, y_train), (x_val,
y_val), (x_test,
y_test) = load_hits(n_samples_by_class=10000,
test_size=0.20,
val_size=0.10,
return_val=True,
channels_to_get=[2])
print(x_train.shape)
print(x_val.shape)
print(x_test.shape)
transformer = Transformer(8, 8)
n, k = (10, 4)
mdl = create_wide_residual_network(input_shape=x_train.shape[1:],
num_classes=transformer.n_transforms,
depth=n,
widen_factor=k)
mdl.compile(optimizer='adam',
loss='categorical_crossentropy',