def conv_gru(inpts, mem, kw, kh, nmaps, cutoff, prefix):
 def conv_lin(args, suffix, bias_start):
  return conv_linear(args, kw, kh, (len(args) * nmaps), nmaps, True, bias_start, ((prefix + '/') + suffix))
 reset = sigmoid_cutoff(conv_lin((inpts + [mem]), 'r', 1.0), cutoff)
 candidate = tf.tanh(conv_lin((inpts + [(reset * mem)]), 'c', 0.0))
 gate = sigmoid_cutoff(conv_lin((inpts + [mem]), 'g', 1.0), cutoff)
 return ((gate * mem) + ((1 - gate) * candidate))

def conv_gru ( inpts , mem , kw , kh , nmaps , cutoff , prefix ) : def conv_lin ( args , suffix , bias_start ) : return conv_linear ( args , kw , kh , ( len ( args ) * nmaps ) , nmaps , True , bias_start , ( ( prefix + '/' ) + suffix ) ) reset = sigmoid_cutoff ( conv_lin ( ( inpts + [ mem ] ) , 'r' , 1.0 ) , cutoff ) candidate = tf . tanh ( conv_lin ( ( inpts + [ ( reset * mem ) ] ) , 'c' , 0.0 ) ) gate = sigmoid_cutoff ( conv_lin ( ( inpts + [ mem ] ) , 'g' , 1.0 ) , cutoff ) return ( ( gate * mem ) + ( ( 1 - gate ) * candidate ) )
convolutional gru .