Advanced Deep Learning with Keras

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Chapter 1Figure 1.3.3: MLP MNIST digit classifier modelFigure 1.3.4: The MLP MNIST digit classifier in Figure 1.3.3 is made up of fully connected layers. For simplicity,the activation and dropout are not shown. One unit or perceptron is also shown.[ 9 ]
Introducing Advanced Deep Learning with KerasListing 1.3.2, mlp-mnist-1.3.2.py shows the Keras implementation of the MNISTdigit classifier model using MLP:import numpy as npfrom keras.models import Sequentialfrom keras.layers import Dense, Activation, Dropoutfrom 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)# image dimensions (assumed square)image_size = x_train.shape[1]input_size = image_size * image_size# resize and normalizex_train = np.reshape(x_train, [-1, input_size])x_train = x_train.astype('float32') / 255x_test = np.reshape(x_test, [-1, input_size])x_test = x_test.astype('float32') / 255# network parametersbatch_size = 128hidden_units = 256dropout = 0.45# model is a 3-layer MLP with ReLU and dropout after each layermodel = Sequential()model.add(Dense(hidden_units, input_dim=input_size))model.add(Activation('relu'))model.add(Dropout(dropout))model.add(Dense(hidden_units))model.add(Activation('relu'))model.add(Dropout(dropout))model.add(Dense(num_labels))# this is the output for one-hot vector[ 10 ]
Page 2 and 3: Advanced Deep Learningwith KerasApp
Page 4 and 5: mapt.ioMapt is an online digital li
Page 6 and 7: I would like to thank my family, Ch
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 28 and 29: Chapter 1model.add(Activation('soft
Page 30 and 31: Chapter 1model.add(Activation('relu
Page 32 and 33: Chapter 1As an example, l2 weight r
Page 34 and 35: [ 17 ]Chapter 1How far the predicte
Page 36 and 37: Chapter 1Figure 1.3.8: Plot of a fu
Page 38 and 39: Chapter 1The highest test accuracy
Page 40 and 41: Chapter 1Figure 1.3.9: The graphica
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
Page 52 and 53: Chapter 1Layers Optimizer Regulariz
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Page 57 and 58: Deep Neural NetworksWhile this chap
Page 59 and 60: Deep Neural Networks# reshape and n
Page 61 and 62: Deep Neural NetworksEverything else
Page 63 and 64: Deep Neural Networksfrom keras.util
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Deep Neural NetworksResNet v2 is al
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Deep Neural Networks…if version =
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Deep Neural NetworksTo prevent the
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Deep Neural NetworksAverage Pooling
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Deep Neural Networks# orig paper us
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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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