keras model example

keras_model_example.py

from keras.layers import Convolution2D, MaxPooling2D, Dense, Dropout, Activation, Flatten, Input
from keras.models import Model
from keras.optimizers import RMSprop

batchsize = 64
conv_num_filters_1 = 16
conv_num_filters_2 = 32
conv_num_filters_3 = 32

filter_size = 4

pool_size_1 = 4
pool_size_2 = 4
pool_size_3 = 4

dp_rate = 0.5

fc_unit1 = 128
fc_unit2 = 64
out_class = 3

padd = 'valid'
activation_func = 'relu'

input_data = Input(shape=(1, obs_size, len(features_name)), name='the_input', dtype='float32')

inner = Convolution2D(conv_num_filters_1, kernel_size=(1, filter_size), 
                      padding=padd, activation=activation_func, name='conv1')(input_data)
inner = Convolution2D(conv_num_filters_1, kernel_size=(1, filter_size),
                      padding=padd, activation=activation_func, name='conv1_2')(inner)
inner = MaxPooling2D(pool_size=(1, pool_size_1), name='max1')(inner)
inner = Dropout(dp_rate, name='dropout1')(inner)

inner = Convolution2D(conv_num_filters_2, kernel_size=(1, filter_size), 
                      padding=padd, activation=activation_func, name='conv2')(inner)
inner = Convolution2D(conv_num_filters_2, kernel_size=(1, filter_size),
                      padding=padd, activation=activation_func, name='conv2_2')(inner)
inner = MaxPooling2D(pool_size=(1, pool_size_2), name='max2')(inner)
inner = Dropout(dp_rate, name='dropout2')(inner)

inner = Convolution2D(conv_num_filters_3, kernel_size=(1, filter_size), 
                      padding=padd, activation=activation_func, name='conv3')(inner)
inner = Convolution2D(conv_num_filters_3, kernel_size=(1, filter_size),
                      padding=padd, activation=activation_func, name='conv3_2')(inner)
inner = MaxPooling2D(pool_size=(1, pool_size_3), name='max3')(inner)
inner = Dropout(dp_rate, name='dropout3')(inner)

inner = Flatten(name='flatten')(inner)
inner = Dense(fc_unit1, activation='relu', name='FC1')(inner)
inner = Dropout(dp_rate, name='dropoutfc')(inner)

y_pred = Dense(out_class, activation='softmax', name='FC2')(inner)
model = Model(inputs=[input_data], outputs=[y_pred])
model.summary()