AI News, AI ‘judge’ doesn’t explain why it reaches certaindecisions

AI ‘judge’ doesn’t explain why it reaches certaindecisions

A few weeks ago, it was announced that Keras would be getting official Google support and would become part of the TensorFlow machine learning library.

Given my previous posts on implementing an XOR-solving neural network in a variety of different languages and tools, I thought it was time to see what it would look like in Keras.

The goal is to create a neural network that will correctly predict the values 0 or 1, depending on the inputs x1 and x2 as shown.

Assuming you’ve already installed Keras, we’ll start with setting up the classification problem and the expected outputs: So far, so good.

Finally, we apply a loss function and learning mode for Keras to be able to adjust the neural network: In this example, we’ll use the standard Mean Squared Error loss function and Stochastic Gradient Descent optimiser.

All going well, the network weights will converge on a solution that can correctly classify the inputs (if not, you may need to up the number of epochs): Clearly this network is on it’s way to converging on the original expected outputs we defined above (y).

Getting started with the Keras functional API

The Keras functional API is the way to go for defining complex models, such as multi-output models, directed acyclic graphs, or models with shared layers.

The main input to the model will be the headline itself, as a sequence of words, but to spice things up, our model will also have an auxiliary input, receiving extra data such as the time of day when the headline was posted, etc. The

At this point, we feed into the model our auxiliary input data by concatenating it with the LSTM output: This defines a model with two inputs and two outputs: We compile the model and assign a weight of 0.2 to the auxiliary loss. To

a sequence of 280 vectors of size 256, where each dimension in the 256-dimensional vector encodes the presence/absence of a character (out of an alphabet of 256 frequent characters).

To share a layer across different inputs, simply instantiate the layer once, then call it on as many inputs as you want: Let's pause to take a look at how to read the shared layer's output or output shape.

Whenever you are calling a layer on some input, you are creating a new tensor (the output of the layer), and you are adding a 'node' to the layer, linking the input tensor to the output tensor.

The same is true for the properties input_shape and output_shape: as long as the layer has only one node, or as long as all nodes have the same input/output shape, then the notion of 'layer output/input shape' is well defined, and that one shape will be returned by layer.output_shape/layer.input_shape.

But if, for instance, you apply the same Conv2D layer to an input of shape (32, 32, 3), and then to an input of shape (64, 64, 3), the layer will have multiple input/output shapes, and you will have to fetch them by specifying the index of the node they belong to: Code examples are still the best way to get started, so here are a few more.

The Keras Blog

The only trick here is to normalize the gradient of the pixels of the input image, which avoids very small and very large gradients and ensures a smooth gradient ascent process.

We can use the same code to systematically display what sort of input (they're not unique) maximizes each filter in each layer, giving us a neat visualization of the convnet's modular-hierarchical decomposition of its visual space.

This means that we could potentially compress the number of filters used in a convnet by a large factor by finding a way to make the convolution filters rotation-invariant.

In the highest layers (block5_conv2, block5_conv3) we start to recognize textures similar to that found in the objects that network was trained to classify, such as feathers, eyes, etc.

What it means is that we should refrain from our natural tendency to anthropomorphize them and believe that they 'understand', say, the concept of dog, or the appearance of a magpie, just because they are able to classify these objects with high accuracy.

Two things: first, they understand a decomposition of their visual input space as a hierarchical-modular network of convolution filters, and second, they understand a probabilitistic mapping between certain combinations of these filters and a set of arbitrary labels.

Of course, one would expect the visual cortex to learn something similar, to the extent that this constitutes a 'natural' decomposition of our visual world (in much the same way that the Fourier decomposition would be a 'natural' decomposition of a periodic audio signal).

The visual cortex is not convolutional to begin with, and while it is structured in layers, the layers are themselves structured into cortical columns whose exact purpose is still not well understood --a feature not found in our artificial networks (although Geoff Hinton is working on it).

Besides, there is so much more to visual perception than the classification of static pictures --human perception is fundamentally sequential and active, not static and passive, and is tightly intricated with motor control (e.g.

Today we have better tools to map complex information spaces than we ever did before, which is awesome, but at the end of the day they are tools, not creatures, and none of what they do could reasonably qualify as 'thinking'.

That said, visualizing what convnets learn is quite fascinating --who would have guessed that simple gradient descent with a reasonable loss function over a sufficiently large dataset would be enough to learn this beautiful hierarchical-modular network of patterns that manages to explain a complex visual space surprisingly well.

Develop Your First Neural Network in Python With Keras Step-By-Step

Keras is a powerful easy-to-use Python library for developing and evaluating deep learning models.

It wraps the efficient numerical computation libraries Theano and TensorFlow and allows you to define and train neural network models in a few short lines of code.

The steps you are going to cover in this tutorial are as follows: This tutorial has a few requirements: If you need help with your environment, see the tutorial: Create a new file called and type or copy-and-paste the code into the file as you go.

This makes it easy to use directly with neural networks that expect numerical input and output values, and ideal for our first neural network in Keras.

We can specify the number of neurons in the layer as the first argument, the initialization method as the second argument as init and specify the activation function using the activation argument.

In this case, we initialize the network weights to a small random number generated from a uniform distribution (‘uniform‘), in this case between 0 and 0.05 because that is the default uniform weight initialization in Keras.

We use a sigmoid on the output layer to ensure our network output is between 0 and 1 and easy to map to either a probability of class 1 or snap to a hard classification of either class with a default threshold of 0.5.

The second hidden layer has 8 neurons and finally, the output layer has 1 neuron to predict the class (onset of diabetes or not).

The backend automatically chooses the best way to represent the network for training and making predictions to run on your hardware, such as CPU or GPU or even distributed.

We must specify the loss function to use to evaluate a set of weights, the optimizer used to search through different weights for the network and any optional metrics we would like to collect and report during training.

The training process will run for a fixed number of iterations through the dataset called epochs, that we must specify using the nepochs argument.

We can also set the number of instances that are evaluated before a weight update in the network is performed, called the batch size and set using the batch_size argument.

You can evaluate your model on your training dataset using the evaluate() function on your model and pass it the same input and output used to train the model.

This will generate a prediction for each input and output pair and collect scores, including the average loss and any metrics you have configured, such as accuracy.

Running this example, you should see a message for each of the 150 epochs printing the loss and accuracy for each, followed by the final evaluation of the trained model on the training dataset.

Specifically, you learned the five key steps in using Keras to create a neural network or deep learning model, step-by-step including: Do you have any questions about Keras or about this tutorial? Ask

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