def batch_normalization():
def compile_fn(di, dh):
bn = keras.layers.BatchNormalization()
def fn(di):
return {'out' : bn(di['in'])}
return fn
return siso_tfm('BatchNormalization', compile_fn, {})
def dropout(h_keep_prob):
def compile_fn(di, dh):
Dropout = keras.layers.Dropout(dh['keep_prob'])
def fn(di):
return {'out' : Dropout(di['in'])}
return fn
return siso_tfm('Dropout', compile_fn, {'keep_prob' : h_keep_prob})
def dense(h_units):
def compile_fn(di, dh):
Dense = keras.layers.Dense(dh['units'])
def fn(di):
return {'out' : Dense(di['in'])}
return fn
return siso_tfm('Dense', compile_fn, {'units' : h_units})
def flatten():
def compile_fn(di, dh):
Flatten = keras.layers.Flatten()
def fn(di):
return {'out': Flatten(di['in'])}
return fn
return siso_tfm('Flatten', compile_fn, {}) # use siso_tfm for now
def dense(h_units):
def compile_fn(di, dh): # compile function
Dense = tf.keras.layers.Dense(dh['units'])
def fn(di): # forward function
return {'out': Dense(di['in'])}
return fn
return siso_tfm('Dense', compile_fn, {'units': h_units})
def nonlinearity(h_nonlin_name):
def compile_fn(di, dh):
def fn(di):
nonlin_name = dh['nonlin_name']
if nonlin_name == 'relu':
Out = keras.layers.Activation('relu')(di['in'])
elif nonlin_name == 'tanh':
Out = keras.layers.Activation('tanh')(di['in'])
elif nonlin_name == 'elu':
Out = keras.layers.Activation('elu')(di['in'])
else:
raise ValueError
return {"out" : Out}
return fn
return siso_tfm('Nonlinearity', compile_fn, {'nonlin_name' : h_nonlin_name})