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',