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| author | Jules Laplace <julescarbon@gmail.com> | 2020-02-12 16:47:34 +0100 |
|---|---|---|
| committer | Jules Laplace <julescarbon@gmail.com> | 2020-02-12 16:47:34 +0100 |
| commit | 92b210979f501e3a9a857b614abdda3786942627 (patch) | |
| tree | 99a0d917c937d588361e5fb370378c768da7e0f4 /cli/app | |
| parent | 7a9be5eff266bc180fffa3b0e28b12a0dbcca2a3 (diff) | |
deeper pass
Diffstat (limited to 'cli/app')
| -rw-r--r-- | cli/app/search/search_dense.py | 8 |
1 files changed, 6 insertions, 2 deletions
diff --git a/cli/app/search/search_dense.py b/cli/app/search/search_dense.py index 85adc2f..a35ab07 100644 --- a/cli/app/search/search_dense.py +++ b/cli/app/search/search_dense.py @@ -281,14 +281,17 @@ def find_dense_embedding_for_images(params, opt_tag="inverse_" + timestamp(), op trained_params += layer_label_variables optimizer = tf.train.AdamOptimizer(learning_rate=lrate, beta1=0.9, beta2=0.999) - inv_train_op = optimizer.minimize(inv_loss, var_list=trained_params, - global_step=inv_step) + inv_train_op = optimizer.minimize(inv_loss, var_list=trained_params, global_step=inv_step) reinit_optimizer = tf.variables_initializer(optimizer.variables()) optimizer_quad = tf.train.AdamOptimizer(learning_rate=lrate, beta1=0.9, beta2=0.999) inv_train_op_quad = optimizer_quad.minimize(inv_loss_quad, var_list=trained_params, global_step=inv_step) reinit_optimizer_quad = tf.variables_initializer(optimizer_quad.variables()) + optimizer_quint = tf.train.AdamOptimizer(learning_rate=lrate, beta1=0.9, beta2=0.999) + inv_train_op_quint = optimizer_quint.minimize(inv_loss_quint, var_list=trained_params, global_step=inv_step) + reinit_optimizer_quint = tf.variables_initializer(optimizer_quint.variables()) + # -------------------------- # Noise source. # -------------------------- @@ -373,6 +376,7 @@ def find_dense_embedding_for_images(params, opt_tag="inverse_" + timestamp(), op encoding_init_funcs = [ reinit_optimizer, reinit_optimizer_quad, + reinit_optimizer_quint, ] if params.inv_layer != 'latent': |