pytorch2keras/activation_layers.py
def convert_sigmoid(params, w_name, scope_name, inputs, layers, weights, names):
    """
    Convert sigmoid 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 sigmoid ...')

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

    sigmoid = keras.layers.Activation('sigmoid', name=tf_name)
    layers[scope_name] = sigmoid(layers[inputs[0]])
def convert_sigmoid(params, w_name, scope_name, inputs, layers, weights, names):
    """
    Convert sigmoid 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 sigmoid ...')

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

    sigmoid = keras.layers.Activation('sigmoid', name=tf_name)
    layers[scope_name] = sigmoid(layers[inputs[0]])
Convert sigmoid 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