baselines/common/models.py
def conv_only(convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)], **conv_kwargs):
    '''
    convolutions-only net

    Parameters:
    ----------

    conv:       list of triples (filter_number, filter_size, stride) specifying parameters for each layer.

    Returns:

    function that takes tensorflow tensor as input and returns the output of the last convolutional layer

    '''

    def network_fn(X):
        out = tf.cast(X, tf.float32) / 255.
        with tf.variable_scope("convnet"):
            for num_outputs, kernel_size, stride in convs:
                out = layers.convolution2d(out,
                                           num_outputs=num_outputs,
                                           kernel_size=kernel_size,
                                           stride=stride,
                                           activation_fn=tf.nn.relu,
                                           **conv_kwargs)

        return out
    return network_fn
def conv_only(convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)], **conv_kwargs):
    '''
    convolutions-only net

    Parameters:
    ----------

    conv:       list of triples (filter_number, filter_size, stride) specifying parameters for each layer.

    Returns:

    function that takes tensorflow tensor as input and returns the output of the last convolutional layer

    '''

    def network_fn(X):
        out = tf.cast(X, tf.float32) / 255.
        with tf.variable_scope("convnet"):
            for num_outputs, kernel_size, stride in convs:
                out = layers.convolution2d(out,
                                           num_outputs=num_outputs,
                                           kernel_size=kernel_size,
                                           stride=stride,
                                           activation_fn=tf.nn.relu,
                                           **conv_kwargs)

        return out
    return network_fn
convolutions-only net

    Parameters:
    ----------

    conv:       list of triples (filter_number, filter_size, stride) specifying parameters for each layer.

    Returns:

    function that takes tensorflow tensor as input and returns the output of the last convolutional layer