Advanced Deep Learning with Keras

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Chapter 10Figure 10.7.8: The total rewards received per episode using the A2C methodIn the experiments conducted, we used the same learning rate, 1e-3, for logprobability and value networks optimization. The discount factor is set to 0.99,except for A2C which is easier to train at a 0.95 discount factor.The reader is encouraged to run the trained network by executing:$ python3 policygradient-car-10.1.1.py--encoder_weights=encoder_weights.h5 --actor_weights=actor_weights.h5Following table shows other modes of running policygradient-car-10.1.1.py.The weights file (that is, *.h5) can be replaced by your own pre-trained weights file.Please consult the code to see the other potential options:PurposeTrain REINFORCEfrom scratchTrain REINFORCEwith baseline fromscratchTrain Actor-Criticfrom scratchTrain A2C fromscratchRunpython3 policygradient-car-10.1.1.py--encoder_weights=encoder_weights.h5python3 policygradient-car-10.1.1.py--encoder_weights=encoder_weights.h5 -bpython3 policygradient-car-10.1.1.py--encoder_weights=encoder_weights.h5 -apython3 policygradient-car-10.1.1.py--encoder_weights=encoder_weights.h5 -c[ 339 ]
Policy Gradient MethodsTrain REINFORCEfrom previouslysaved weightsTrain REINFORCEwith baseline frompreviously savedweightsTrain Actor-Criticfrom previouslysaved weightsTrain A2C frompreviously savedweightspython3 policygradient-car-10.1.1.py--encoder_weights=encoder_weights.h5--actor_weights=actor_weights.h5 --trainpython3 policygradient-car-10.1.1.py--encoder_weights=encoder_weights.h5--actor_weights=actor_weights.h5--value_weights=value_weights.h5 -b --trainpython3 policygradient-car-10.1.1.py--encoder_weights=encoder_weights.h5--actor_weights=actor_weights.h5--value_weights=value_weights.h5 -a --trainpython3 policygradient-car-10.1.1.py--encoder_weights=encoder_weights.h5--actor_weights=actor_weights.h5--value_weights=value_weights.h5 -c --trainTable 10.7.1: Different options in running policygradient-car-10.1.1.pyAs a final note, the implementation of the policy gradient methods in Keras hassome limitations. For example, training the actor model requires resampling theaction. The action is first sampled and applied to the environment to observe thereward and next state. Then, another sample is taken for training the log probabilitymodel. The second sample is not necessarily the same as the first one, but the rewardthat is used for training comes from the first sampled action, which can introducestochastic error in the computation of gradients.The good news is Keras is gaining a lot of support from TensorFlow in the formof tf.keras. Transitioning from Keras to a more flexible and powerful machinelearning library, like TensorFlow, has been made a lot easier. If you started withKeras and wanted to build low-level custom machine learning routines, the APIsof Keras and tf.keras share strong similarities.There is a small learning curve in using Keras in TensorFlow. Furthermore, in tf.keras, you're able to take advantage of the new easy to use Dataset and EstimatorsAPIs of TensorFlow. This simplifies a lot of the code and model reuse that endsup with a clean pipeline. With the new eager execution mode of TensorFlow, itbecomes even easier to implement and debug Python codes in tf.keras andTensorFlow. Eager execution allows the execution of codes without buildinga computational graph as we did in this book. It also allows code structuressimilar to a typical Python program.[ 340 ]
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Advanced Deep Learningwith KerasApp
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mapt.ioMapt is an online digital li
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I would like to thank my family, Ch
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Table of ContentsPrefaceVChapter 1:
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[ iii ]Table of ContentsChapter 7:
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[ v ]PrefaceIn recent years, deep l
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Chapter 5, Improved GANs, covers al
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def encoder_layer(inputs,filters=16
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Introducing Advanced DeepLearning w
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Chapter 1Installing Keras and Tenso
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Chapter 1• RNNs: Recurrent neural
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[ 7 ]Chapter 1In the preceding figu
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Chapter 1Figure 1.3.3: MLP MNIST di
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Chapter 1model.add(Activation('soft
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Chapter 1model.add(Activation('relu
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Chapter 1As an example, l2 weight r
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[ 17 ]Chapter 1How far the predicte
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Chapter 1Figure 1.3.8: Plot of a fu
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Chapter 1The highest test accuracy
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Chapter 1Figure 1.3.9: The graphica
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Chapter 1# image is processed as is
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Chapter 1The computation involved i
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Chapter 1Listing 1.4.2 shows a summ
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Chapter 164-64-64 RMSprop Dropout(0
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Chapter 1There are the two main dif
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Chapter 1Layers Optimizer Regulariz
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ConclusionThis chapter provided an
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Deep Neural NetworksWhile this chap
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Deep Neural Networks# reshape and n
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Deep Neural NetworksEverything else
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Deep Neural Networksfrom keras.util
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Deep Neural NetworksFigure 2.1.3: T
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Deep Neural NetworksHence, the netw
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Deep Neural NetworksGenerally speak
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Deep Neural NetworksIn the dataset,
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Deep Neural NetworksTransition Laye
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Deep Neural NetworksThere are some
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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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Page 360 and 361: Other Books YouMay EnjoyIf you enjo
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