Advanced Deep Learning with Keras

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Chapter 1Figure 1.3.9: The graphical description of the MLP MNIST digit classifierConvolutional neural networks (CNNs)We're now going to move onto the second artificial neural network, ConvolutionalNeural Networks (CNNs). In this section, we're going solve the same MNIST digitclassification problem, instead this time using CNNs.[ 23 ]
Introducing Advanced Deep Learning with KerasFigure 1.4.1 shows the CNN model that we'll use for the MNIST digit classification,while its implementation is illustrated in Listing 1.4.1. Some changes in the previousmodel will be needed to implement the CNN model. Instead of having input vector,the input tensor now has new dimensions (height, width, channels) or (image_size,image_size, 1) = (28, 28, 1) for the grayscale MNIST images. Resizing the train andtest images will be needed to conform to this input shape requirement.Figure 1.4.1: CNN model for MNIST digit classificationListing 1.4.1, cnn-mnist-1.4.1.py shows the Keras code for the MNIST digitclassification using CNN:import numpy as npfrom keras.models import Sequentialfrom keras.layers import Activation, Dense, Dropoutfrom keras.layers import Conv2D, MaxPooling2D, Flattenfrom keras.utils import to_categorical, plot_modelfrom keras.datasets import mnist# load mnist dataset(x_train, y_train), (x_test, y_test) = mnist.load_data()# compute the number of labelsnum_labels = len(np.unique(y_train))# convert to one-hot vectory_train = to_categorical(y_train)y_test = to_categorical(y_test)# input image dimensionsimage_size = x_train.shape[1]# resize and normalizex_train = np.reshape(x_train,[-1, image_size, image_size, 1])x_test = np.reshape(x_test,[-1, image_size, image_size, 1])x_train = x_train.astype('float32') / 255x_test = x_test.astype('float32') / 255# network parameters[ 24 ]
Page 2 and 3: Advanced Deep Learningwith KerasApp
Page 4 and 5: mapt.ioMapt is an online digital li
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Page 8 and 9: Table of ContentsPrefaceVChapter 1:
Page 10 and 11: [ iii ]Table of ContentsChapter 7:
Page 12 and 13: [ v ]PrefaceIn recent years, deep l
Page 14 and 15: Chapter 5, Improved GANs, covers al
Page 16 and 17: def encoder_layer(inputs,filters=16
Page 18 and 19: Introducing Advanced DeepLearning w
Page 20 and 21: Chapter 1Installing Keras and Tenso
Page 22 and 23: Chapter 1• RNNs: Recurrent neural
Page 24 and 25: [ 7 ]Chapter 1In the preceding figu
Page 26 and 27: Chapter 1Figure 1.3.3: MLP MNIST di
Page 28 and 29: Chapter 1model.add(Activation('soft
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Page 32 and 33: Chapter 1As an example, l2 weight r
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Page 38 and 39: Chapter 1The highest test accuracy
Page 42 and 43: Chapter 1# image is processed as is
Page 44 and 45: Chapter 1The computation involved i
Page 46 and 47: Chapter 1Listing 1.4.2 shows a summ
Page 48 and 49: Chapter 164-64-64 RMSprop Dropout(0
Page 50 and 51: Chapter 1There are the two main dif
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Page 61 and 62: Deep Neural NetworksEverything else
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Page 88 and 89: AutoencodersIn the previous chapter
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Chapter 3The autoencoder has the te
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Chapter 3Firstly, we're going to im
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Chapter 3# reconstruct the inputout
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Chapter 3Figure 3.2.2: The decoder
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batch_size=32,model_name="autoencod
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Chapter 3Figure 3.2.6: Digits gener
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Chapter 3As shown in Figure 3.3.2,
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Chapter 3image_size = x_train.shape
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Chapter 3# Mean Square Error (MSE)
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Chapter 3from keras.layers import R
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Chapter 3# build the autoencoder mo
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Chapter 3x_train,validation_data=(x
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Chapter 3ConclusionIn this chapter,
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Generative Adversarial Networks (GA
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Improved GANsIn summary, the goal o
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Improved GANsThe intuition behind E
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Improved GANsThis makes sense since
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Improved GANsIn the context of GANs
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Improved GANsFigure 5.1.3: Top: Tra
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Improved GANsThe functions include:
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Improved GANsmodels = (generator, d
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Improved GANsfor layer in discrimin
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Improved GANsFollowing figure shows
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Improved GANsThe preceding table sh
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Improved GANsFollowing figure shows
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Improved GANsEssentially, in CGAN w
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Improved GANslayer = Dense(layer_fi
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Improved GANsx = BatchNormalization
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Improved GANsdiscriminator.compile(
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Improved GANssize=batch_size)real_i
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Improved GANsUnlike CGAN, the sampl
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Improved GANsConclusionIn this chap
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Disentangled Representation GANsIn
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Disentangled Representation GANsInf
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Disentangled Representation GANsFol
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Disentangled Representation GANs# A
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Disentangled Representation GANsif
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Disentangled Representation GANsLis
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Disentangled Representation GANsdat
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Disentangled Representation GANsy[b
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Disentangled Representation GANspyt
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Disentangled Representation GANsThe
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Disentangled Representation GANsSta
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Disentangled Representation GANs( )
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Disentangled Representation GANsThe
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Disentangled Representation GANsfea
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Disentangled Representation GANs# f
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Disentangled Representation GANslat
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Disentangled Representation GANsDis
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Disentangled Representation GANsz_d
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Disentangled Representation GANs2.
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Disentangled Representation GANsFig
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Cross-Domain GANsIn computer vision
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Chapter 7There are many more exampl
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The CycleGAN ModelFigure 7.1.3 show
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Chapter 7Repeat for n training step
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Chapter 7Implementing CycleGAN usin
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filters=16,kernel_size=3,strides=2,
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Chapter 7kernel_size=kernel_size)e3
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Listing 7.1.3, cyclegan-7.1.1.py sh
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Chapter 71) Build target and source
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Chapter 7preal_target,reco_source,r
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size=batch_size)real_source = sourc
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Chapter 7returndirs=dirs,show=True)
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Chapter 7Figure 7.1.10: Color (from
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[ 229 ]Chapter 7titles = ('MNIST pr
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Chapter 7Figure 7.1.13: Style trans
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Chapter 7Figure 7.1.15: The backwar
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Chapter 7References1. Yuval Netzer
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Variational Autoencoders (VAEs)In t
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Variational Autoencoders (VAEs)Typi
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Variational Autoencoders (VAEs)For
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Variational Autoencoders (VAEs)VAEs
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Variational Autoencoders (VAEs)outp
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Variational Autoencoders (VAEs)Figu
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Variational Autoencoders (VAEs)The
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Variational Autoencoders (VAEs)Prec
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Variational Autoencoders (VAEs)shap
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Variational Autoencoders (VAEs)cvae
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Variational Autoencoders (VAEs)In F
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Variational Autoencoders (VAEs)The
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Deep ReinforcementLearningReinforce
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[ 273 ]Chapter 9Formally, the RL pr
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Chapter 9Where:( ) ( , )∗V s maxQ
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Chapter 9Initially, the agent assum
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Chapter 9Figure 9.3.6: Assuming the
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Q-Learning in PythonThe environment
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Chapter 9----------------"""self.re
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Chapter 9# UI to dump Q Table conte
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Chapter 9Figure 9.3.10: The value f
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Chapter 9Figure 9.5.1: Frozen lake
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Chapter 9# discount factorself.gamm
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Chapter 9# training of Q Tableif do
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Chapter 9Where all terms are famili
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Listing 9.6.1 shows us the DQN impl
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Chapter 9if self.ddqn:print("------
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Chapter 9updates# correction on the
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QmaxChapter 9⎧rj+1if episodetermi
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References1. Sutton and Barto. Rein
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Policy Gradient MethodsPolicy gradi
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Policy Gradient MethodsGiven a cont
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Policy Gradient MethodsRequire: Dis
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Policy Gradient MethodsRequire: θ
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Policy Gradient MethodsThe state is
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Policy Gradient Methodsself.encoder
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Policy Gradient MethodsThe policy n
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Policy Gradient MethodsFigure 10.6.
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Policy Gradient MethodsAfter buildi
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Policy Gradient Methods[_, _, _, re
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Policy Gradient MethodsEach algorit
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Policy Gradient Methodswhile not do
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Policy Gradient MethodsTrain REINFO
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Other Books YouMay EnjoyIf you enjo
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Other Books You May EnjoyLeave a re
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DenseNet 39DenseNet-BC (Bottleneck-
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VVariational Autoencoder (VAE)about
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Advanced Deep Learning with Keras

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