def convert_tanh(params, w_name, scope_name, inputs, layers, weights, names):
    """
    Convert tanh layer.

    Args:
        params: dictionary with layer parameters
        w_name: name prefix in state_dict
        scope_name: pytorch scope name
        inputs: pytorch node inputs
        layers: dictionary with keras tensors
        weights: pytorch state_dict
        names: use short names for keras layers
    """
    print('Converting tanh ...')

    if names == 'short':
        tf_name = 'TANH' + random_string(4)
    elif names == 'keep':
        tf_name = w_name
    else:
        tf_name = w_name + str(random.random())

    tanh = keras.layers.Activation('tanh', name=tf_name)
    layers[scope_name] = tanh(layers[inputs[0]])
def convert_tanh(params, w_name, scope_name, inputs, layers, weights, names):
    
    print('Converting tanh ...')

    if names == 'short':
        tf_name = 'TANH' + random_string(4)
    elif names == 'keep':
        tf_name = w_name
    else:
        tf_name = w_name + str(random.random())

    tanh = keras.layers.Activation('tanh', name=tf_name)
    layers[scope_name] = tanh(layers[inputs[0]])
def convert_tanh ( params , w_name , scope_name , inputs , layers , weights , names ) : print ( 'Converting tanh ...' ) if names == 'short' : tf_name = 'TANH' + random_string ( 4 ) elif names == 'keep' : tf_name = w_name else : tf_name = w_name + str ( random . random ( ) ) tanh = keras . layers . Activation ( 'tanh' , name = tf_name ) layers [ scope_name ] = tanh ( layers [ inputs [ 0 ] ] )
Convert tanh layer.

    Args:
        params: dictionary with layer parameters
        w_name: name prefix in state_dict
        scope_name: pytorch scope name
        inputs: pytorch node inputs
        layers: dictionary with keras tensors
        weights: pytorch state_dict
        names: use short names for keras layers
Convert tanh layer .