Book Image

Advanced Deep Learning with Keras

By : Rowel Atienza
Book Image

Advanced Deep Learning with Keras

By: Rowel Atienza

Overview of this book

Recent developments in deep learning, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Deep Reinforcement Learning (DRL) are creating impressive AI results in our news headlines - such as AlphaGo Zero beating world chess champions, and generative AI that can create art paintings that sell for over $400k because they are so human-like. Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. Using Keras as an open-source deep learning library, you'll find hands-on projects throughout that show you how to create more effective AI with the latest techniques. The journey begins with an overview of MLPs, CNNs, and RNNs, which are the building blocks for the more advanced techniques in the book. You’ll learn how to implement deep learning models with Keras and TensorFlow 1.x, and move forwards to advanced techniques, as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You then learn all about GANs, and how they can open new levels of AI performance. Next, you’ll get up to speed with how VAEs are implemented, and you’ll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans - a major stride forward for modern AI. To complete this set of advanced techniques, you'll learn how to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.
Table of Contents (13 chapters)

Recurrent neural networks (RNNs)

We're now going to look at the last of our three artificial neural networks, Recurrent neural networks, or RNNs.

RNNs are a family of networks that are suitable for learning representations of sequential data like text in Natural Language Processing (NLP) or stream of sensor data in instrumentation. While each MNIST data sample is not sequential in nature, it is not hard to imagine that every image can be interpreted as a sequence of rows or columns of pixels. Thus, a model based on RNNs can process each MNIST image as a sequence of 28-element input vectors with timesteps equal to 28. The following listing shows the code for the RNN model in Figure 1.5.1:

Recurrent neural networks (RNNs)

Figure 1.5.1: RNN model for MNIST digit classification

In the following listing, Listing 1.5.1, the shows the Keras code for MNIST digit classification using RNNs:

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Activation, SimpleRNN
from keras.utils import to_categorical, plot_model
from keras.datasets import mnist
# load mnist dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# compute the number of labels
num_labels = len(np.unique(y_train))

# convert to one-hot vector
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)

# resize and normalize
image_size = x_train.shape[1]
x_train = np.reshape(x_train,[-1, image_size, image_size])
x_test = np.reshape(x_test,[-1, image_size, image_size])
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

# network parameters
input_shape = (image_size, image_size)
batch_size = 128
units = 256
dropout = 0.2 
# model is RNN with 256 units, input is 28-dim vector 28 timesteps
model = Sequential()
plot_model(model, to_file='rnn-mnist.png', show_shapes=True)

# loss function for one-hot vector
# use of sgd optimizer
# accuracy is good metric for classification tasks
# train the network, y_train, epochs=20, batch_size=batch_size)

loss, acc = model.evaluate(x_test, y_test, batch_size=batch_size)
print("\nTest accuracy: %.1f%%" % (100.0 * acc))

There are the two main differences between RNNs and the two previous models. First is the input_shape = (image_size, image_size) which is actually input_shape = (timesteps, input_dim) or a sequence of input_dim—dimension vectors of timesteps length. Second is the use of a SimpleRNN layer to represent an RNN cell with units=256. The units variable represents the number of output units. If the CNN is characterized by the convolution of kernel across the input feature map, the RNN output is a function not only of the present input but also of the previous output or hidden state. Since the previous output is also a function of the previous input, the current output is also a function of the previous output and input and so on. The SimpleRNN layer in Keras is a simplified version of the true RNN. The following, equation describes the output of SimpleRNN:

ht = tanh(b + Wht-1 + Uxt) (1.5.1)

In this equation, b is the bias, while W and U are called recurrent kernel (weights for previous output) and kernel (weights for the current input) respectively. Subscript t is used to indicate the position in the sequence. For SimpleRNN layer with units=256, the total number of parameters is 256 + 256 × 256 + 256 × 28 = 72,960 corresponding to b, W, and U contributions.

Following figure shows the diagrams of both SimpleRNN and RNN that were used in the MNIST digit classification. What makes SimpleRNN simpler than RNN is the absence of the output values Ot = Vht + c before the softmax is computed:

Recurrent neural networks (RNNs)

Figure 1.5.2: Diagram of SimpleRNN and RNN

RNNs might be initially harder to understand when compared to MLPs or CNNs. In MLPs, the perceptron is the fundamental unit. Once the concept of the perceptron is understood, MLPs are just a network of perceptrons. In CNNs, the kernel is a patch or window that slides through the feature map to generate another feature map. In RNNs, the most important is the concept of self-loop. There is in fact just one cell.

The illusion of multiple cells appears because a cell exists per timestep but in fact, it is just the same cell reused repeatedly unless the network is unrolled. The underlying neural networks of RNNs are shared across cells.

The summary in Listing 1.5.2 indicates that using a SimpleRNN requires a fewer number of parameters. Figure 1.5.3 shows the graphical description of the RNN MNIST digit classifier. The model is very concise. Table 1.5.1 shows that the SimpleRNN has the lowest accuracy among the networks presented.

Listing 1.5.2, RNN MNIST digit classifier summary:

Layer (type)                 Output Shape              Param #   
simple_rnn_1 (SimpleRNN)     (None, 256)               72960     
dense_1 (Dense)              (None, 10)                2570      
activation_1 (Activation)    (None, 10)                0         
Total params: 75,530
Trainable params: 75,530
Non-trainable params: 0
Recurrent neural networks (RNNs)

Figure 1.5.3: The RNN MNIST digit classifier graphical description




Train Accuracy, %

Test Accuracy, %





















Table 1.5.1: The different SimpleRNN network configurations and performance measures

In many deep neural networks, other members of the RNN family are more commonly used. For example, Long Short-Term Memory (LSTM) networks have been used in both machine translation and question answering problems. LSTM networks address the problem of long-term dependency or remembering relevant past information to the present output.

Unlike RNNs or SimpleRNN, the internal structure of the LSTM cell is more complex. Figure 1.5.4 shows a diagram of LSTM in the context of MNIST digit classification. LSTM uses not only the present input and past outputs or hidden states; it introduces a cell state, st, that carries information from one cell to the other. Information flow between cell states is controlled by three gates, ft, it and qt. The three gates have the effect of determining which information should be retained or replaced and the amount of information in the past and current input that should contribute to the current cell state or output. We will not discuss the details of the internal structure of the LSTM cell in this book. However, an intuitive guide to LSTM can be found at:

The LSTM() layer can be used as a drop-in replacement to SimpleRNN(). If LSTM is overkill for the task at hand, a simpler version called Gated Recurrent Unit (GRU) can be used. GRU simplifies LSTM by combining the cell state and hidden state together. GRU also reduces the number of gates by one. The GRU() function can also be used as a drop-in replacement for SimpleRNN().

Recurrent neural networks (RNNs)

Figure 1.5.4: Diagram of LSTM. The parameters are not shown for clarity

There are many other ways to configure RNNs. One way is making an RNN model that is bidirectional. By default, RNNs are unidirectional in the sense that the current output is only influenced by the past states and the current input. In bidirectional RNNs, future states can also influence the present state and the past states by allowing information to flow backward. Past outputs are updated as needed depending on the new information received. RNNs can be made bidirectional by calling a wrapper function. For example, the implementation of bidirectional LSTM is Bidirectional(LSTM()).

For all types of RNNs, increasing the units will also increase the capacity. However, another way of increasing the capacity is by stacking the RNN layers. You should note though that as a general rule of thumb, the capacity of the model should only be increased if needed. Excess capacity may contribute to overfitting, and as a result, both longer training time and slower performance during prediction.