def input_node():
def compile_fn(di, dh):
def fn(di):
return {'out': layers.Input(di['in'])}
return fn
return siso_keras_module('input', compile_fn, {})
def dropout(h_keep_prob):
def compile_fn(di, dh):
layer = layers.Dropout(dh['keep_prob'])
def fn(di):
return {'out': layer(di['in'])}
return fn
return siso_keras_module('Dropout', compile_fn, {'keep_prob': h_keep_prob})
def fc_layer(h_num_units):
def compile_fn(di, dh):
layer = layers.Dense(dh['num_units'])
def fn(di):
return {'out': layer(di['in'])}
return fn
return siso_keras_module('FCLayer', compile_fn, {'num_units': h_num_units})
def global_pool2d():
def compile_fn(di, dh):
layer = layers.GlobalAveragePooling2D()
def fn(di):
return {'out': layer(di['in'])}
return fn
return siso_keras_module('GlobalAveragePool', compile_fn, {})
def batch_normalization():
def compile_fn(di, dh):
layer = layers.BatchNormalization()
def fn(di):
return {'out': layer(di['in'])}
return fn
return siso_keras_module('BatchNormalization', compile_fn, {})
def activation(h_activation):
def compile_fn(di, dh):
layer = layers.Activation(dh['activation'])
def fn(di):
return {'out': layer(di['in'])}
return fn
return siso_keras_module('Activation', compile_fn,
{'activation': h_activation})
def avg_pool2d(h_kernel_size, h_stride):
def compile_fn(di, dh):
layer = layers.AveragePooling2D(pool_size=dh['kernel_size'],
strides=(dh['stride'], dh['stride']),
padding='same')
def fn(di):
return {'out': layer(di['in'])}
return fn
return siso_keras_module('AvgPool2D', compile_fn, {
'kernel_size': h_kernel_size,
'stride': h_stride,
})
def nonlinearity(h_nonlin_name):
def compile_fn(di, dh):
def fn(di):
nonlin_name = dh['nonlin_name']
if nonlin_name == 'relu':
Out = Activation('relu')(di['in'])
elif nonlin_name == 'tanh':
Out = Activation('tanh')(di['in'])
elif nonlin_name == 'elu':
Out = Activation('elu')(di['in'])
else:
raise ValueError
return {"out": Out}
return fn
return hke.siso_keras_module('Nonlinearity', compile_fn,
{'nonlin_name': h_nonlin_name})
def conv2d(h_num_filters, h_filter_width, h_stride, h_use_bias):
def compile_fn(di, dh):
layer = layers.Conv2D(dh['num_filters'], (dh['filter_width'], ) * 2,
strides=(dh['stride'], ) * 2,
use_bias=dh['use_bias'],
padding='SAME')
def fn(di):
return {'out': layer(di['in'])}
return fn
return siso_keras_module(
'Conv2D', compile_fn, {
'num_filters': h_num_filters,
'filter_width': h_filter_width,
'stride': h_stride,
'use_bias': h_use_bias,
})