keras
Introduction
1. Examples
2. Optimizers
3. Objectives
4. Models
5. Activations
6. Initializations
7. Regularizers
8. Constraints
9. Callbacks
10. Datasets
11. Layers, Core Layers
12. Layers, Convolutional Layers
13. Layers, Recurrent Layers
14. Layers, Advanced Activations Layers
15. Layers, Normalization Layers
16. Layers, Embedding Layers
17. Layers, Containers
18. Preprocessing, Sequence Preprocessing
19. Preprocessing, Text Preprocessing
20. Preprocessing, Image Preprocessing
21. Utils, Visualization Utilities

keras

Convolution1D

keras.layers.convolutional.Convolution1D(nb_filter, stack_size, filter_length, 
        init='uniform', activation='linear', weights=None, 
        image_shape=None, border_mode='valid', subsample_length=1, 
        W_regularizer=None, b_regularizer=None, W_constraint=None, 
        b_constraint=None)

Convolution operator for filtering neighborhoods of one-dimensional inputs.

Convolution2D

keras.layers.convolutional.Convolution2D(nb_filter, stack_size, nb_row, nb_col, 
        init='glorot_uniform', activation='linear', weights=None, 
        image_shape=None, border_mode='valid', subsample=(1,1))

Convolution operator for filtering windows of two-dimensional inputs. This is a wrapper for Theano's conv2d.

MaxPooling1D

keras.layers.convolutional.MaxPooling1D(pool_length=2, ignore_border=True)

MaxPooling2D

keras.layers.convolutional.MaxPooling2D(poolsize=(2, 2), ignore_border=True)

This is a wrapper for Theano's max_pool_2d.