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| author | Jules Laplace <julescarbon@gmail.com> | 2020-01-08 02:02:56 +0100 |
|---|---|---|
| committer | Jules Laplace <julescarbon@gmail.com> | 2020-01-08 02:02:56 +0100 |
| commit | 4d05266f3c1f62646a5948f532528f591c6dc2ee (patch) | |
| tree | 3e517d0d1f388fb8704ee1475f5e066f9ff6c396 | |
| parent | 2034d4c0cd241106900273980ee84f808a73d196 (diff) | |
up
| -rw-r--r-- | cli/app/search/search_class.py | 8 |
1 files changed, 3 insertions, 5 deletions
diff --git a/cli/app/search/search_class.py b/cli/app/search/search_class.py index 134a139..691a0a8 100644 --- a/cli/app/search/search_class.py +++ b/cli/app/search/search_class.py @@ -144,13 +144,11 @@ def find_nearest_vector(sess, generator, opt_fp_in, opt_dims, out_images, out_la feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [batch_size, -1]) feat_loss += tf.reduce_mean(feat_square_diff) / len(opt_feature_layers) - # Batch reconstruction error. - inv_loss = 1.0 * mse_loss + 1.0 * feat_loss - + loss = 1.0 * mse_loss + 1.0 * feat_loss train_step_z = tf.train.AdamOptimizer(learning_rate=z_lr, beta1=0.9, beta2=0.999) \ - .minimize(inv_loss, var_list=[input_z]) + .minimize(loss, var_list=[input_z]) train_step_y = tf.train.AdamOptimizer(learning_rate=y_lr, beta1=0.9, beta2=0.999) \ - .minimize(inv_loss, var_list=[input_y]) + .minimize(loss, var_list=[input_y]) else: loss = tf.compat.v1.losses.mean_squared_error(target, output) |