diff --git a/dump_graph_model_v4.py b/dump_graph_model_v4.py index 5fe045a..ed8eb50 100644 --- a/dump_graph_model_v4.py +++ b/dump_graph_model_v4.py @@ -53,7 +53,7 @@ def preprocess(x): return tf.concat([preprocess_features(x), latent_input], axis=-1) def postprocess(x): - x = 10 ** x - 1 + x = 10**x - 1 return tf.where(x < 1.0, 0.0, x) dump_graph.model_to_graph( diff --git a/legacy_code/models/baseline_fc_v4_8x16.py b/legacy_code/models/baseline_fc_v4_8x16.py index e27a143..8e32935 100644 --- a/legacy_code/models/baseline_fc_v4_8x16.py +++ b/legacy_code/models/baseline_fc_v4_8x16.py @@ -199,7 +199,7 @@ def gradient_penalty_on_data(self, features, real): # if self.cramer: # d_real = tf.norm(d_real, axis=-1) grads = tf.reshape(t.gradient(d_real, real), [len(real), -1]) - return tf.reduce_mean(tf.reduce_sum(grads ** 2, axis=-1)) + return tf.reduce_mean(tf.reduce_sum(grads**2, axis=-1)) @tf.function def calculate_losses(self, feature_batch, target_batch): diff --git a/legacy_code/models/baseline_v2_10x10.py b/legacy_code/models/baseline_v2_10x10.py index adf23ca..8940342 100644 --- a/legacy_code/models/baseline_v2_10x10.py +++ b/legacy_code/models/baseline_v2_10x10.py @@ -189,7 +189,7 @@ def gradient_penalty_on_data(self, features, real): # if self.cramer: # d_real = tf.norm(d_real, axis=-1) grads = tf.reshape(t.gradient(d_real, real), [len(real), -1]) - return tf.reduce_mean(tf.reduce_sum(grads ** 2, axis=-1)) + return tf.reduce_mean(tf.reduce_sum(grads**2, axis=-1)) @tf.function def calculate_losses(self, feature_batch, target_batch): diff --git a/legacy_code/models/baseline_v3_6x15.py b/legacy_code/models/baseline_v3_6x15.py index 0486529..e4e0f61 100644 --- a/legacy_code/models/baseline_v3_6x15.py +++ b/legacy_code/models/baseline_v3_6x15.py @@ -212,7 +212,7 @@ def gradient_penalty_on_data(self, features, real): # if self.cramer: # d_real = tf.norm(d_real, axis=-1) grads = tf.reshape(t.gradient(d_real, real), [len(real), -1]) - return tf.reduce_mean(tf.reduce_sum(grads ** 2, axis=-1)) + return tf.reduce_mean(tf.reduce_sum(grads**2, axis=-1)) @tf.function def calculate_losses(self, feature_batch, target_batch): diff --git a/legacy_code/models/baseline_v4_8x16.py b/legacy_code/models/baseline_v4_8x16.py index 2e37e94..30bc756 100644 --- a/legacy_code/models/baseline_v4_8x16.py +++ b/legacy_code/models/baseline_v4_8x16.py @@ -221,7 +221,7 @@ def gradient_penalty_on_data(self, features, real): # if self.cramer: # d_real = tf.norm(d_real, axis=-1) grads = tf.reshape(t.gradient(d_real, real), [len(real), -1]) - return tf.reduce_mean(tf.reduce_sum(grads ** 2, axis=-1)) + return tf.reduce_mean(tf.reduce_sum(grads**2, axis=-1)) @tf.function def calculate_losses(self, feature_batch, target_batch): diff --git a/legacy_code/test_script_data_v0.py b/legacy_code/test_script_data_v0.py index 77dbd72..6f7cb3d 100644 --- a/legacy_code/test_script_data_v0.py +++ b/legacy_code/test_script_data_v0.py @@ -27,7 +27,7 @@ def unscale(x): - return 10 ** x - 1 + return 10**x - 1 def write_hist_summary(step): diff --git a/legacy_code/test_script_data_v1.py b/legacy_code/test_script_data_v1.py index e2a1b55..99a1fcd 100644 --- a/legacy_code/test_script_data_v1.py +++ b/legacy_code/test_script_data_v1.py @@ -58,7 +58,7 @@ def save_model(step): writer_val = tf.summary.create_file_writer(f'logs/{args.checkpoint_name}/validation') def unscale(x): - return 10 ** x - 1 + return 10**x - 1 def write_hist_summary(step): if step % args.save_every == 0: diff --git a/legacy_code/test_script_data_v1_normed.py b/legacy_code/test_script_data_v1_normed.py index a83d391..69eeab5 100644 --- a/legacy_code/test_script_data_v1_normed.py +++ b/legacy_code/test_script_data_v1_normed.py @@ -31,7 +31,7 @@ def unscale(x): - return 10 ** x - 1 + return 10**x - 1 def write_hist_summary(step): diff --git a/legacy_code/test_script_data_v2.py b/legacy_code/test_script_data_v2.py index d500df5..609e5ad 100644 --- a/legacy_code/test_script_data_v2.py +++ b/legacy_code/test_script_data_v2.py @@ -138,7 +138,7 @@ def save_model(step): writer_val = tf.summary.create_file_writer(f'logs/{args.checkpoint_name}/validation') def unscale(x): - return 10 ** x - 1 + return 10**x - 1 def get_images(return_raw_data=False, calc_chi2=False, gen_more=None, sample=(X_test, Y_test), batch_size=128): X, Y = sample diff --git a/legacy_code/test_script_data_v3.py b/legacy_code/test_script_data_v3.py index b6ea43f..5d79c34 100644 --- a/legacy_code/test_script_data_v3.py +++ b/legacy_code/test_script_data_v3.py @@ -134,7 +134,7 @@ def save_model(step): writer_val = tf.summary.create_file_writer(f'logs/{args.checkpoint_name}/validation') def unscale(x): - return 10 ** x - 1 + return 10**x - 1 def get_images(return_raw_data=False, calc_chi2=False, gen_more=None, sample=(X_test, Y_test), batch_size=128): X, Y = sample diff --git a/metrics/trends.py b/metrics/trends.py index 3121971..4f901d6 100644 --- a/metrics/trends.py +++ b/metrics/trends.py @@ -31,7 +31,7 @@ def stats(arr): ).T if do_plot: - mean_p_std_err = (mean_err ** 2 + std_err ** 2) ** 0.5 + mean_p_std_err = (mean_err**2 + std_err**2) ** 0.5 plt.fill_between(bin_centers, mean - mean_err, mean + mean_err, **kwargs) kwargs['alpha'] *= 0.5 kwargs = {k: v for k, v in kwargs.items() if k != 'label'} @@ -78,11 +78,11 @@ def make_trend_plot(feature_real, real, feature_gen, gen, name, calc_chi2=False, gen_upper = gen_mean + gen_std gen_lower = gen_mean - gen_std - gen_err2 = gen_mean_err ** 2 + gen_std_err ** 2 + gen_err2 = gen_mean_err**2 + gen_std_err**2 real_upper = real_mean + real_std real_lower = real_mean - real_std - real_err2 = real_mean_err ** 2 + real_std_err ** 2 + real_err2 = real_mean_err**2 + real_std_err**2 chi2 = ((gen_upper - real_upper) ** 2 / (gen_err2 + real_err2)).sum() + ( (gen_lower - real_lower) ** 2 / (gen_err2 + real_err2) diff --git a/models/callbacks.py b/models/callbacks.py index 3574b33..86c9cb2 100644 --- a/models/callbacks.py +++ b/models/callbacks.py @@ -56,6 +56,6 @@ def get_scheduler(lr, lr_decay): return eval(lr_decay) def schedule_lr(step): - return lr * lr_decay ** step + return lr * lr_decay**step return schedule_lr diff --git a/models/model_v4.py b/models/model_v4.py index 850061b..b4d44e9 100644 --- a/models/model_v4.py +++ b/models/model_v4.py @@ -145,7 +145,7 @@ def gradient_penalty_on_data(self, features, real): d_real = self.discriminator([_f(features), real]) grads = tf.reshape(t.gradient(d_real, real), [len(real), -1]) - return tf.reduce_mean(tf.reduce_sum(grads ** 2, axis=-1)) + return tf.reduce_mean(tf.reduce_sum(grads**2, axis=-1)) @tf.function def calculate_losses(self, feature_batch, target_batch): diff --git a/models/nn.py b/models/nn.py index fb94b90..42e3486 100644 --- a/models/nn.py +++ b/models/nn.py @@ -170,7 +170,7 @@ def vector_img_connect_block(vector_shape, img_shape, block, vector_bypass=False if concat_outputs: outputs = tf.keras.layers.Concatenate(axis=-1)(outputs) - args = dict(inputs=[input_vec, input_img], outputs=outputs,) + args = dict(inputs=[input_vec, input_img], outputs=outputs) if name: args['name'] = name diff --git a/models/scalers.py b/models/scalers.py index bf91442..25dec81 100644 --- a/models/scalers.py +++ b/models/scalers.py @@ -16,7 +16,7 @@ def scale(self, x): return np.log10(1 + x) def unscale(self, x): - return 10 ** x - 1 + return 10**x - 1 class Gaussian: