Man pages for tpq/caress
Caress, a Gentler Introduction to Keras
| ascend | Perform A Single Gradient Ascent |
| build | Train a Keras Model |
| constraint_all_ones | Constrain All Weights to One |
| constraint_all_zeros | Constrain All Weights to Zero |
| constraint_cols_to_unit_sum | Constrain Columns to Unit Sum |
| constraint_rows_to_unit_sum | Constrain Rows to Unit Sum |
| constraint_runif | Constrain All Weights to Randomize |
| from_input | Create Input Layer |
| get_incoming_layer_name | Get Incoming Layer Name |
| get_layer_gradient | Get the Gradient Associated with a Layer |
| get_layer_loss | Get the Loss Associated with a Layer |
| get_layer_names | Get Layer Names |
| get_layer_output | Get Layer Output |
| get_layer_weights | Get Layer Weights |
| layer2index | Find Index for Layer Name |
| layer_kernel_conv2d | Perform Kernel Convolution |
| layer_kernel_dot | Perform Kernel Dot Product |
| layer_learnable_array | Initialize Learnable Layer |
| layer_orthogonal_to | Create Orthogonal Layer |
| layer_pairwise_residual | Create Pairwise Layer |
| layer_pairwise_rmse | Create Pairwise Layer |
| layer_pseudo_embed | Create an Embedding Matrix |
| layer_to_dense_DeepTRIAGE | Apply a DeepTRIAGE Layer |
| layer_to_dense_stereo | Split Model into Two Parallel Layers |
| layer_to_dense_stereo_and_add | Split Model into Two Parallel Layers |
| layer_to_dense_stereo_and_cat | Split Model into Two Parallel Layers |
| layer_to_dense_stereo_and_diff | Split Model into Two Parallel Layers |
| model_decode | Decode Output of Any Layer |
| model_mirror | Copy Weights from Another Model |
| prepare | Prepare a Keras Model |
| sample_random | Split the Training and Test Set |
| set_layer_weights | Set Layer Weights |
| to_categorical_invert | Make Categorical Mask |
| to_categorical_mask | Make Categorical Mask |
| to_loss | Get Loss for Output |
| to_metric | Get Metric for Output |
| to_output | Create Output Layer |
| type_of_y | Get Type for Y |
