Model construction utilities based on keras
import warnings
from distutils.version import LooseVersion
import keras
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten

from .model import Model, NoSuchLayerError

if LooseVersion(keras.__version__) >= LooseVersion('2.0.0'):
    from keras.layers import Conv2D
    from keras.layers import Convolution2D

def conv_2d(filters, kernel_shape, strides, padding, input_shape=None):
    Defines the right convolutional layer according to the
    version of Keras that is installed.
    :param filters: (required integer) the dimensionality of the output
                    space (i.e. the number output of filters in the
    :param kernel_shape: (required tuple or list of 2 integers) specifies
                         the strides of the convolution along the width and
    :param padding: (required string) can be either 'valid' (no padding around
                    input or feature map) or 'same' (pad to ensure that the
                    output feature map size is identical to the layer input)
    :param input_shape: (optional) give input shape if this is the first
                        layer of the model
    :return: the Keras layer
    if LooseVersion(keras.__version__) >= LooseVersion('2.0.0'):
        if input_shape is not None:
            return Conv2D(filters=filters, kernel_size=kernel_shape,
                          strides=strides, padding=padding,
            return Conv2D(filters=filters, kernel_size=kernel_shape,
                          strides=strides, padding=padding)
        if input_shape is not None:
            return Convolution2D(filters, kernel_shape[0], kernel_shape[1],
                                 subsample=strides, border_mode=padding,
            return Convolution2D(filters, kernel_shape[0], kernel_shape[1],
                                 subsample=strides, border_mode=padding)

def cnn_model(logits=False, input_ph=None, img_rows=28, img_cols=28,
              channels=1, nb_filters=64, nb_classes=10):
    Defines a CNN model using Keras sequential model
    :param logits: If set to False, returns a Keras model, otherwise will also
                    return logits tensor
    :param input_ph: The TensorFlow tensor for the input
                    (needed if returning logits)
                    ("ph" stands for placeholder but it need not actually be a
    :param img_rows: number of row in the image
    :param img_cols: number of columns in the image
    :param channels: number of color channels (e.g., 1 for MNIST)
    :param nb_filters: number of convolutional filters per layer
    :param nb_classes: the number of output classes
    model = Sequential()

    # Define the layers successively (convolution layers are version dependent)
    if keras.backend.image_dim_ordering() == 'th':
        input_shape = (channels, img_rows, img_cols)
        input_shape = (img_rows, img_cols, channels)

    layers = [conv_2d(nb_filters, (8, 8), (2, 2), "same",
              conv_2d((nb_filters * 2), (6, 6), (2, 2), "valid"),
              conv_2d((nb_filters * 2), (5, 5), (1, 1), "valid"),

    for layer in layers:

    if logits:
        logits_tensor = model(input_ph)

    if logits:
        return model, logits_tensor
        return model

class KerasModelWrapper(Model):
    An implementation of `Model` that wraps a Keras model. It
    specifically exposes the hidden features of a model by creating new models.
    The symbolic graph is reused and so there is little overhead. Splitting
    in-place operations can incur an overhead.

    def __init__(self, model):
        Create a wrapper for a Keras model
        :param model: A Keras model
        super(KerasModelWrapper, self).__init__(None, None, {})

        if model is None:
            raise ValueError('model argument must be supplied.')

        self.model = model
        self.keras_model = None

    def _get_softmax_name(self):
        Looks for the name of the softmax layer.
        :return: Softmax layer name
        for i, layer in enumerate(self.model.layers):
            cfg = layer.get_config()
            if 'activation' in cfg and cfg['activation'] == 'softmax':
                return layer.name

        raise Exception("No softmax layers found")

    def _get_logits_name(self):
        Looks for the name of the layer producing the logits.
        :return: name of layer producing the logits
        softmax_name = self._get_softmax_name()
        softmax_layer = self.model.get_layer(softmax_name)

        if not isinstance(softmax_layer, Activation):
            # In this case, the activation is part of another layer
            return softmax_name

        if hasattr(softmax_layer, 'inbound_nodes'):
                "Please update your version to keras >= 2.1.3; "
                "support for earlier keras versions will be dropped on "
            node = softmax_layer.inbound_nodes[0]
            node = softmax_layer._inbound_nodes[0]

        logits_name = node.inbound_layers[0].name

        return logits_name

    def get_logits(self, x):
        :param x: A symbolic representation of the network input.
        :return: A symbolic representation of the logits
        logits_name = self._get_logits_name()
        logits_layer = self.get_layer(x, logits_name)

        # Need to deal with the case where softmax is part of the
        # logits layer
        if logits_name == self._get_softmax_name():
            softmax_logit_layer = self.get_layer(x, logits_name)

            # The final op is the softmax. Return its input
            logits_layer = softmax_logit_layer._op.inputs[0]

        return logits_layer

    def get_probs(self, x):
        :param x: A symbolic representation of the network input.
        :return: A symbolic representation of the probs
        name = self._get_softmax_name()

        return self.get_layer(x, name)

    def get_layer_names(self):
        :return: Names of all the layers kept by Keras
        layer_names = [x.name for x in self.model.layers]
        return layer_names

    def fprop(self, x):
        Exposes all the layers of the model returned by get_layer_names.
        :param x: A symbolic representation of the network input
        :return: A dictionary mapping layer names to the symbolic
                 representation of their output.
        from keras.models import Model as KerasModel

        if self.keras_model is None:
            # Get the input layer
            new_input = self.model.get_input_at(0)

            # Make a new model that returns each of the layers as output
            out_layers = [x_layer.output for x_layer in self.model.layers]
            self.keras_model = KerasModel(new_input, out_layers)

        # and get the outputs for that model on the input x
        outputs = self.keras_model(x)

        # Keras only returns a list for outputs of length >= 1, if the model
        # is only one layer, wrap a list
        if len(self.model.layers) == 1:
            outputs = [outputs]

        # compute the dict to return
        fprop_dict = dict(zip(self.get_layer_names(), outputs))

        return fprop_dict

    def get_layer(self, x, layer):
        Expose the hidden features of a model given a layer name.
        :param x: A symbolic representation of the network input
        :param layer: The name of the hidden layer to return features at.
        :return: A symbolic representation of the hidden features
        :raise: NoSuchLayerError if `layer` is not in the model.
        # Return the symbolic representation for this layer.
        output = self.fprop(x)
            requested = output[layer]
        except KeyError:
            raise NoSuchLayerError()
        return requested