2D Convolution-Batch Normalization-Activation stack builder # Arguments inputs (tensor): input tensor from input image or previous layer

ResNet Version 1 Model builder [a] Stacks of 2 x (3 x 3) Conv2D-BN-ReLU Last ReLU is after the shortcut connection. At the beginning of

ResNet Version 2 Model builder [b] Stacks of (1 x 1)-(3 x 3)-(1 x 1) BN-ReLU-Conv2D or also known as bottleneck layer First shortcut co

Compute the accuracy of a TF model on some data :param sess: TF session to use :param x: input placeholder :param y: output placeholder (

:param x: A symbolic representation of the network input. :return: A symbolic representation of the probs

Looks for the name of the softmax layer. :return: Softmax layer name

Exposes all the layers of the model returned by get_layer_names. :param x: A symbolic representation of the network input :return: A dict

Expose the hidden features of a model given a layer name. :param x: A symbolic representation of the network input :param layer: The name

:return: Names of all the layers kept by Keras

Compute the accuracy of a TF model on some data :param sess: TF session to use :param x: input placeholder :param y: output placeholder (

Compute the accuracy of a TF model on some data :param sess: TF session to use :param x: input placeholder :param y: output placeholder (

Create a wrapper for a Keras model :param model: A Keras model

Looks for the name of the layer producing the logits. :return: name of layer producing the logits

Compute the accuracy of a TF model on some data :param sess: TF session to use :param x: input placeholder :param y: output placeholder (

:param x: A symbolic representation of the network input. :return: A symbolic representation of the logits