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CHAINER.VARIABLE
Note. It only supports types that are supported by CUDA’s atomicAdd when an integer array is included in slices.The supported types are numpy.float32, numpy.int32, numpy.uint32, numpy.uint64 andnumpy.ulonglong.
TRAINER EXTENSIONS
3. priority¶. As a Trainer object can be assigned multiple Extension objects, the execution order is defined according to the following three values:. PRIORITY_WRITER: The priority for extensions that write some records to the observation dictionary.It includes cases that the extension directly adds values to the observation dictionary, or the extension uses the chainer.report() function to CHAINER.LINKS.LINEAR Parameters. n_repeat – Number of times to repeat.. mode – It should be either init, copy, or share. init means parameters of each repeated element in the returned Sequential will be re-initialized, so that all elements have different initial parameters. copy means that the parameters will not be re-initialized but object itself will be deep-copied, so that all elements have same initial CHAINER.USING_CONFIG chainer.using_config¶ chainer.using_config (name, value, config = chainer.config) ¶ Context manager to temporarily change the thread-local configuration. Parameters. name – Name of the configuration to change.. value – Temporary value of the configuration entry.. config (LocalConfig) – Configuration object.Chainer’s thread-local configuration is used by default. CHAINERRL - DEEP REINFORCEMENT LEARNING LIBRARYSEE MORE ON CHAINER.ORG CHAINER.FUNCTIONS.ACCURACY chainer.functions.accuracy¶ chainer.functions.accuracy (y, t, ignore_label = None) ¶ Computes multiclass classification accuracy of the minibatch CHAINER.TRAINING.EXTENSIONS.DUMPGRAPH Parameters. root_name – Name of the root of the computational graph.The root variable is retrieved by this name from the observation dictionary of the trainer. filename – Output file name.For historical reasons out_name is also accepted as an alias of this argument.. variable_style – Dot node style for variables.Each variable is rendered by an octagon by default. CHAINER.DATASETS.TRANSFORMDATASET chainer.datasets.TransformDataset¶ class chainer.datasets.TransformDataset (dataset, transform) ¶. Dataset that indexes the base dataset and transforms the data. This dataset wraps the base dataset by modifying the behavior of the base dataset’s __getitem__().Arrays returned by __getitem__() of the base dataset with an integer as an argument are transformed by the givenfunction
CHAINER.ITERATORS.SERIALITERATOR chainer.iterators.SerialIterator¶ class chainer.iterators. SerialIterator (dataset, batch_size, repeat = True, shuffle = None, order_sampler = None) ¶. Dataset iterator that serially reads the examples. This is a simple implementation of Iterator that just visits each example in either the order of indexes or a shuffled order.. To avoid unintentional performance degradation, the RECURRENT NETS AND THEIR COMPUTATIONAL GRAPH Recurrent Nets¶. Recurrent nets are neural networks with loops. They are often used to learn from sequential input/output. Given an input stream \(x_1, x_2, \dots, x_t, \dots\) and the initial state \(h_0\), a recurrent net iteratively updates its state by \(h_t = f(x_t, h_{t-1})\), and at some or every point in time \(t\), it outputs \(y_t= g(h_t)\).
CHAINER.VARIABLE
Note. It only supports types that are supported by CUDA’s atomicAdd when an integer array is included in slices.The supported types are numpy.float32, numpy.int32, numpy.uint32, numpy.uint64 andnumpy.ulonglong.
TRAINER EXTENSIONS
3. priority¶. As a Trainer object can be assigned multiple Extension objects, the execution order is defined according to the following three values:. PRIORITY_WRITER: The priority for extensions that write some records to the observation dictionary.It includes cases that the extension directly adds values to the observation dictionary, or the extension uses the chainer.report() function to CHAINER.LINKS.LINEAR Parameters. n_repeat – Number of times to repeat.. mode – It should be either init, copy, or share. init means parameters of each repeated element in the returned Sequential will be re-initialized, so that all elements have different initial parameters. copy means that the parameters will not be re-initialized but object itself will be deep-copied, so that all elements have same initial CHAINER.USING_CONFIG chainer.using_config¶ chainer.using_config (name, value, config = chainer.config) ¶ Context manager to temporarily change the thread-local configuration. Parameters. name – Name of the configuration to change.. value – Temporary value of the configuration entry.. config (LocalConfig) – Configuration object.Chainer’s thread-local configuration is used by default. CHAINERRL - DEEP REINFORCEMENT LEARNING LIBRARYSEE MORE ON CHAINER.ORG CHAINER.FUNCTIONS.ACCURACY chainer.functions.accuracy¶ chainer.functions.accuracy (y, t, ignore_label = None) ¶ Computes multiclass classification accuracy of the minibatch CHAINER.TRAINING.EXTENSIONS.DUMPGRAPH Parameters. root_name – Name of the root of the computational graph.The root variable is retrieved by this name from the observation dictionary of the trainer. filename – Output file name.For historical reasons out_name is also accepted as an alias of this argument.. variable_style – Dot node style for variables.Each variable is rendered by an octagon by default. CHAINER.DATASETS.TRANSFORMDATASET chainer.datasets.TransformDataset¶ class chainer.datasets.TransformDataset (dataset, transform) ¶. Dataset that indexes the base dataset and transforms the data. This dataset wraps the base dataset by modifying the behavior of the base dataset’s __getitem__().Arrays returned by __getitem__() of the base dataset with an integer as an argument are transformed by the givenfunction
CHAINER.ITERATORS.SERIALITERATOR chainer.iterators.SerialIterator¶ class chainer.iterators. SerialIterator (dataset, batch_size, repeat = True, shuffle = None, order_sampler = None) ¶. Dataset iterator that serially reads the examples. This is a simple implementation of Iterator that just visits each example in either the order of indexes or a shuffled order.. To avoid unintentional performance degradation, the RECURRENT NETS AND THEIR COMPUTATIONAL GRAPH Recurrent Nets¶. Recurrent nets are neural networks with loops. They are often used to learn from sequential input/output. Given an input stream \(x_1, x_2, \dots, x_t, \dots\) and the initial state \(h_0\), a recurrent net iteratively updates its state by \(h_t = f(x_t, h_{t-1})\), and at some or every point in time \(t\), it outputs \(y_t= g(h_t)\).
CHAINER.LINKS.LINEAR Parameters. n_repeat – Number of times to repeat.. mode – It should be either init, copy, or share. init means parameters of each repeated element in the returned Sequential will be re-initialized, so that all elements have different initial parameters. copy means that the parameters will not be re-initialized but object itself will be deep-copied, so that all elements have same initialINSTALLATION
Note. We are automatically testing Chainer on all the recommended environments above. We cannot guarantee that Chainer works on other environments including Windows and macOS (especially with CUDA support), even if Chainer may seem to be running correctly. CHAINER.LINKS.CLASSIFIER chainer.links.Classifier¶ class chainer.links.Classifier (predictor, lossfun=, accfun=, label_key=-1) ¶. A simple classifier model. This is an example of chain that wraps another chain. It computes the loss and accuracy based on a given input/label pair.CHAINER.LINKS.LSTM
chainer.links.LSTM¶ class chainer.links.LSTM (in_size, out_size = None, lateral_init = None, upward_init = None, bias_init = None, forget_bias_init = None) ¶. Fully-connected LSTM layer. This is a fully-connected LSTM layer as a chain. Unlike the lstm() function, which is defined as a stateless activation function, this chain holds upward and lateral connections as child links. CHAINER.USING_CONFIG chainer.using_config¶ chainer.using_config (name, value, config = chainer.config) ¶ Context manager to temporarily change the thread-local configuration. Parameters. name – Name of the configuration to change.. value – Temporary value of the configuration entry.. config (LocalConfig) – Configuration object.Chainer’s thread-local configuration is used by default.API REFERENCE
Read the Docs v: stable . Versions latest stable v7.7.0 v7.4.0 v7.2.0 v7.1.0 v7.0.0 v6.7.0 v6.6.0 v6.5.0 v6.4.0 CHAINER.FUNCTIONS.UPSAMPLING_2D These are the outputs from the max pooling operation including the resulting indices that will be used to upsample pooled_x.Note that the indices all point to the largest, in CHAINER.OPTIMIZERS.ADAM chainer.optimizers.Adam¶ class chainer.optimizers.Adam (alpha = 0.001, beta1 = 0.9, beta2 = 0.999, eps = 1e-08, eta = 1.0, weight_decay_rate = 0, amsgrad = False, adabound = False, final_lr = 0.1, gamma = 0.001) ¶. Adam optimizer. See: Adam: A Method for Stochastic Optimization Modified for proper weight decay (also called AdamW).AdamW introduces the additional parameters eta and CHAINER.LINKS.CAFFE.CAFFEFUNCTION chainer.links.caffe.CaffeFunction¶ class chainer.links.caffe.CaffeFunction (model_path) ¶. Caffe emulator based on the model file of Caffe. Given a protocol buffers file of a Caffe model, this class loads and emulates it on Variable objects. It supports the official reference models provided by BVLC. CHAINER.FUNCTIONS.MEAN_SQUARED_ERROR Example. 1D array examples: >>> x = np. array (). astype (np. float32) >>> y = np. array (). astype (np. float32) >>> F. mean_squared_error (x CHAINER: A FLEXIBLE FRAMEWORK FOR NEURAL NETWORKSBLOGDOCUMENTATION Chainer supports various network architectures including feed-forward nets, convnets, recurrent nets and recursive nets. It also supports per-batch architectures. CUSTOMIZE YOUR OWN LOGGING When a value is passed to the reporter, an object called observer can be optionally attached. In this case, the name of the observer is added as the prefix of the value name. The observer name should be registered beforehand. Using reporter.scope, you can select which observation to save the observed values.. There are also a global API chainer.report(), which reports observed values with the CHAINER.LINKS.CONVOLUTION2D chainer.links.Convolution2D¶ class chainer.links.Convolution2D (self, in_channels, out_channels, ksize = None, stride = 1, pad = 0, nobias = False, initialW = None, initial_bias = None, *, dilate = 1, groups = 1) ¶. Two-dimensional convolutional layer. This link wraps the convolution_2d() function and holds the filter weight and bias vector as parameters.. The output of thisTRAINER EXTENSIONS
make_extension () is a decorator that adds some attributes to a given function. For example, the simple extension we created above can be written in this form: @training.make_extension(trigger=(10, 'epoch')) def lr_drop(trainer): trainer.updater.get_optimizer('main').lr *= 0.1. The difference between the above example and this is whether it has RELEASED CHAINER/CUPY V6.0.0 Released Chainer/CuPy v6.0.0. By Seiya Tokui; May 16, 2019; In Announcement We have released Chainer and CuPy v6.0.0 today! This is a major release that introduces several new features. CHAINER.TRAINING.EXTENSIONS.SNAPSHOT chainer.training.extensions.snapshot ¶. chainer.training.extensions.snapshot. Returns a trainer extension to take snapshots of the trainer. This extension serializes the trainer object and saves it to the output directory. It is used to support resuming the training loop from the saved state. This extension is called once per epoch by default. ディープラーニング入門:CHAINER チュートリアルTRANSLATE THIS PAGE Chainerの入門に最適なチュートリアルサイト。数学の基礎、プログラミング言語Python
の基礎から、機械学習・ディープラーニングの理論の基礎とコーディングまでを幅広く解説します。Chainerは初学者によるディープラーニングの学習から研究者による最先端のアルゴリズムの実装まで幅広くPYTHON 入門
Chainerの入門に最適なチュートリアルサイト。数学の基礎、プログラミング言語Python
の基礎から、機械学習・ディープラーニングの理論の基礎とコーディングまでを幅広く解説します。Chainerは初学者によるディープラーニングの学習から研究者による最先端のアルゴリズムの実装まで幅広く ニューラルネットワークの基礎 Chainerの入門に最適なチュートリアルサイト。数学の基礎、プログラミング言語Python
の基礎から、機械学習・ディープラーニングの理論の基礎とコーディングまでを幅広く解説します。Chainerは初学者によるディープラーニングの学習から研究者による最先端のアルゴリズムの実装まで幅広く トレーナとエクステンション Chainerの入門に最適なチュートリアルサイト。数学の基礎、プログラミング言語Python
の基礎から、機械学習・ディープラーニングの理論の基礎とコーディングまでを幅広く解説します。Chainerは初学者によるディープラーニングの学習から研究者による最先端のアルゴリズムの実装まで幅広く CHAINER: A FLEXIBLE FRAMEWORK FOR NEURAL NETWORKSBLOGDOCUMENTATION Chainer supports various network architectures including feed-forward nets, convnets, recurrent nets and recursive nets. It also supports per-batch architectures. CUSTOMIZE YOUR OWN LOGGING When a value is passed to the reporter, an object called observer can be optionally attached. In this case, the name of the observer is added as the prefix of the value name. The observer name should be registered beforehand. Using reporter.scope, you can select which observation to save the observed values.. There are also a global API chainer.report(), which reports observed values with the CHAINER.LINKS.CONVOLUTION2D chainer.links.Convolution2D¶ class chainer.links.Convolution2D (self, in_channels, out_channels, ksize = None, stride = 1, pad = 0, nobias = False, initialW = None, initial_bias = None, *, dilate = 1, groups = 1) ¶. Two-dimensional convolutional layer. This link wraps the convolution_2d() function and holds the filter weight and bias vector as parameters.. The output of thisTRAINER EXTENSIONS
make_extension () is a decorator that adds some attributes to a given function. For example, the simple extension we created above can be written in this form: @training.make_extension(trigger=(10, 'epoch')) def lr_drop(trainer): trainer.updater.get_optimizer('main').lr *= 0.1. The difference between the above example and this is whether it has RELEASED CHAINER/CUPY V6.0.0 Released Chainer/CuPy v6.0.0. By Seiya Tokui; May 16, 2019; In Announcement We have released Chainer and CuPy v6.0.0 today! This is a major release that introduces several new features. CHAINER.TRAINING.EXTENSIONS.SNAPSHOT chainer.training.extensions.snapshot ¶. chainer.training.extensions.snapshot. Returns a trainer extension to take snapshots of the trainer. This extension serializes the trainer object and saves it to the output directory. It is used to support resuming the training loop from the saved state. This extension is called once per epoch by default. ディープラーニング入門:CHAINER チュートリアルTRANSLATE THIS PAGE Chainerの入門に最適なチュートリアルサイト。数学の基礎、プログラミング言語Python
の基礎から、機械学習・ディープラーニングの理論の基礎とコーディングまでを幅広く解説します。Chainerは初学者によるディープラーニングの学習から研究者による最先端のアルゴリズムの実装まで幅広くPYTHON 入門
Chainerの入門に最適なチュートリアルサイト。数学の基礎、プログラミング言語Python
の基礎から、機械学習・ディープラーニングの理論の基礎とコーディングまでを幅広く解説します。Chainerは初学者によるディープラーニングの学習から研究者による最先端のアルゴリズムの実装まで幅広く ニューラルネットワークの基礎 Chainerの入門に最適なチュートリアルサイト。数学の基礎、プログラミング言語Python
の基礎から、機械学習・ディープラーニングの理論の基礎とコーディングまでを幅広く解説します。Chainerは初学者によるディープラーニングの学習から研究者による最先端のアルゴリズムの実装まで幅広く トレーナとエクステンション Chainerの入門に最適なチュートリアルサイト。数学の基礎、プログラミング言語Python
の基礎から、機械学習・ディープラーニングの理論の基礎とコーディングまでを幅広く解説します。Chainerは初学者によるディープラーニングの学習から研究者による最先端のアルゴリズムの実装まで幅広く CHAINER.LINKS.CONVOLUTION2D chainer.links.Convolution2D¶ class chainer.links.Convolution2D (self, in_channels, out_channels, ksize = None, stride = 1, pad = 0, nobias = False, initialW = None, initial_bias = None, *, dilate = 1, groups = 1) ¶. Two-dimensional convolutional layer. This link wraps the convolution_2d() function and holds the filter weight and bias vector as parameters.. The output of this CUSTOMIZE YOUR OWN LOGGING When a value is passed to the reporter, an object called observer can be optionally attached. In this case, the name of the observer is added as the prefix of the value name. The observer name should be registered beforehand. Using reporter.scope, you can select which observation to save the observed values.. There are also a global API chainer.report(), which reports observed values with the CHAINER.LINKS.LINEAR chainer.links.Linear. Linear layer (a.k.a. fully-connected layer). This is a link that wraps the linear () function, and holds a weight matrix W and optionally a bias vector b as parameters. If initialW is left to the default value of None, the weight matrix W is initialized with i.i.d. Gaussian samples, each of which has zero mean and CHAINER.OPTIMIZERS.ADAM chainer.optimizers.Adam¶ class chainer.optimizers.Adam (alpha = 0.001, beta1 = 0.9, beta2 = 0.999, eps = 1e-08, eta = 1.0, weight_decay_rate = 0, amsgrad = False, adabound = False, final_lr = 0.1, gamma = 0.001) ¶. Adam optimizer. See: Adam: A Method for Stochastic Optimization Modified for proper weight decay (also called AdamW).AdamW introduces the additional parameters eta and CHAINER.USING_CONFIG chainer.using_config¶ chainer.using_config (name, value, config = chainer.config) ¶ Context manager to temporarily change the thread-local configuration. Parameters. name – Name of the configuration to change.. value – Temporary value of the configuration entry.. config (LocalConfig) – Configuration object.Chainer’s thread-local configuration is used by default. CHAINER.FUNCTIONS.ACCURACY chainer.functions.accuracy ¶. chainer.functions.accuracy. Computes multiclass classification accuracy of the minibatch. y ( Variable or N-dimensional array) – Array whose (i, j, k, )-th element indicates the score of the class j at the (i, k, )-th sample. The prediction label t ^ is calculated by the formula t CHAINER.TRAINING.EXTENSIONS.DUMPGRAPH Parameters. root_name – Name of the root of the computational graph.The root variable is retrieved by this name from the observation dictionary of the trainer. filename – Output file name.For historical reasons out_name is also accepted as an alias of this argument.. variable_style – Dot node style for variables.Each variable is rendered by an octagon by default. DCGAN: GENERATE IMAGES WITH DEEP CONVOLUTIONAL GAN In the initializer __init__, an additional keyword argument models is required as you can see the code below. Also, we use keyword arguments iterator, optimizer and device.It should be noted that the optimizer augment takes a dictionary. The two different models require two different optimizers. To specify the different optimizers for the models, we give a dictionary, {'gen': opt_gen, 'dis RECURRENT NETS AND THEIR COMPUTATIONAL GRAPH Recurrent Nets¶. Recurrent nets are neural networks with loops. They are often used to learn from sequential input/output. Given an input stream \(x_1, x_2, \dots, x_t, \dots\) and the initial state \(h_0\), a recurrent net iteratively updates its state by \(h_t = f(x_t, h_{t-1})\), and at some or every point in time \(t\), it outputs \(y_t= g(h_t)\).
チュートリアル Chainerの入門に最適なチュートリアルサイト。数学の基礎、プログラミング言語Python
の基礎から、機械学習・ディープラーニングの理論の基礎とコーディングまでを幅広く解説します。Chainerは初学者によるディープラーニングの学習から研究者による最先端のアルゴリズムの実装まで幅広くAPI REFERENCE
chainer.set_debug. Visualization of Computational Graph. chainer.computational_graph.build_computational_graph. chainer.computational_graph.ComputationalGraph. Static Subgraph Optimizations: Usage. chainer.static_graph. Basic usage. Calling a static chain multiple times in the same iteration. EffectsINSTALLATION
Note. We are automatically testing Chainer on all the recommended environments above. We cannot guarantee that Chainer works on other environments including Windows and macOS (especially with CUDA support), even if Chainer may seem to be running correctly. CUSTOMIZE YOUR OWN LOGGING When a value is passed to the reporter, an object called observer can be optionally attached. In this case, the name of the observer is added as the prefix of the value name. The observer name should be registered beforehand. Using reporter.scope, you can select which observation to save the observed values.. There are also a global API chainer.report(), which reports observed values with the CHAINER.FUNCTIONS.ACCURACY chainer.functions.accuracy ¶. chainer.functions.accuracy. Computes multiclass classification accuracy of the minibatch. y ( Variable or N-dimensional array) – Array whose (i, j, k, )-th element indicates the score of the class j at the (i, k, )-th sample. The prediction label t ^ is calculated by the formula tTRAINER EXTENSIONS
make_extension () is a decorator that adds some attributes to a given function. For example, the simple extension we created above can be written in this form: @training.make_extension(trigger=(10, 'epoch')) def lr_drop(trainer): trainer.updater.get_optimizer('main').lr *= 0.1. The difference between the above example and this is whether it hasCHAINER.GET_DEVICE
chainer.get_device. Returns a device object. Device specifier. If a chainer.backend.Device instance is given, it is returned intact. Otherwise the following values are supported: A string representing a device. (ex. 'native:0', 'native') A chainerx.Device object. A cupy.cuda.Device object. The string '@numpy'. CHAINER.OPTIMIZERS.ADAM chainer.optimizers.Adam¶ class chainer.optimizers.Adam (alpha = 0.001, beta1 = 0.9, beta2 = 0.999, eps = 1e-08, eta = 1.0, weight_decay_rate = 0, amsgrad = False, adabound = False, final_lr = 0.1, gamma = 0.001) ¶. Adam optimizer. See: Adam: A Method for Stochastic Optimization Modified for proper weight decay (also called AdamW).AdamW introduces the additional parameters eta and CHAINER.FUNCTIONS.UPSAMPLING_2D chainer.functions.upsampling_2d. Upsampling using pooling indices. This function produces an upsampled image using pooling indices. This is the original x before max pooling. These are the outputs from the max pooling operation including the resulting indices that will be used to upsample pooled_x. Note that the indices all point to thelargest
CHAINER.TRAINING.EXTENSIONS.DUMPGRAPH Parameters. root_name – Name of the root of the computational graph.The root variable is retrieved by this name from the observation dictionary of the trainer. filename – Output file name.For historical reasons out_name is also accepted as an alias of this argument.. variable_style – Dot node style for variables.Each variable is rendered by an octagon by default. ディープラーニング入門:CHAINER チュートリアルTRANSLATE THIS PAGE Chainerの入門に最適なチュートリアルサイト。数学の基礎、プログラミング言語Python
の基礎から、機械学習・ディープラーニングの理論の基礎とコーディングまでを幅広く解説します。Chainerは初学者によるディープラーニングの学習から研究者による最先端のアルゴリズムの実装まで幅広くAPI REFERENCE
chainer.set_debug. Visualization of Computational Graph. chainer.computational_graph.build_computational_graph. chainer.computational_graph.ComputationalGraph. Static Subgraph Optimizations: Usage. chainer.static_graph. Basic usage. Calling a static chain multiple times in the same iteration. EffectsINSTALLATION
Note. We are automatically testing Chainer on all the recommended environments above. We cannot guarantee that Chainer works on other environments including Windows and macOS (especially with CUDA support), even if Chainer may seem to be running correctly. CUSTOMIZE YOUR OWN LOGGING When a value is passed to the reporter, an object called observer can be optionally attached. In this case, the name of the observer is added as the prefix of the value name. The observer name should be registered beforehand. Using reporter.scope, you can select which observation to save the observed values.. There are also a global API chainer.report(), which reports observed values with the CHAINER.FUNCTIONS.ACCURACY chainer.functions.accuracy ¶. chainer.functions.accuracy. Computes multiclass classification accuracy of the minibatch. y ( Variable or N-dimensional array) – Array whose (i, j, k, )-th element indicates the score of the class j at the (i, k, )-th sample. The prediction label t ^ is calculated by the formula tTRAINER EXTENSIONS
make_extension () is a decorator that adds some attributes to a given function. For example, the simple extension we created above can be written in this form: @training.make_extension(trigger=(10, 'epoch')) def lr_drop(trainer): trainer.updater.get_optimizer('main').lr *= 0.1. The difference between the above example and this is whether it hasCHAINER.GET_DEVICE
chainer.get_device. Returns a device object. Device specifier. If a chainer.backend.Device instance is given, it is returned intact. Otherwise the following values are supported: A string representing a device. (ex. 'native:0', 'native') A chainerx.Device object. A cupy.cuda.Device object. The string '@numpy'. CHAINER.OPTIMIZERS.ADAM chainer.optimizers.Adam¶ class chainer.optimizers.Adam (alpha = 0.001, beta1 = 0.9, beta2 = 0.999, eps = 1e-08, eta = 1.0, weight_decay_rate = 0, amsgrad = False, adabound = False, final_lr = 0.1, gamma = 0.001) ¶. Adam optimizer. See: Adam: A Method for Stochastic Optimization Modified for proper weight decay (also called AdamW).AdamW introduces the additional parameters eta and CHAINER.FUNCTIONS.UPSAMPLING_2D chainer.functions.upsampling_2d. Upsampling using pooling indices. This function produces an upsampled image using pooling indices. This is the original x before max pooling. These are the outputs from the max pooling operation including the resulting indices that will be used to upsample pooled_x. Note that the indices all point to thelargest
CHAINER.TRAINING.EXTENSIONS.DUMPGRAPH Parameters. root_name – Name of the root of the computational graph.The root variable is retrieved by this name from the observation dictionary of the trainer. filename – Output file name.For historical reasons out_name is also accepted as an alias of this argument.. variable_style – Dot node style for variables.Each variable is rendered by an octagon by default. ディープラーニング入門:CHAINER チュートリアルTRANSLATE THIS PAGE Chainerの入門に最適なチュートリアルサイト。数学の基礎、プログラミング言語Python
の基礎から、機械学習・ディープラーニングの理論の基礎とコーディングまでを幅広く解説します。Chainerは初学者によるディープラーニングの学習から研究者による最先端のアルゴリズムの実装まで幅広くAPI REFERENCE
Read the Docs v: stable . Versions latest stable v7.7.0 v7.4.0 v7.2.0 v7.1.0 v7.0.0 v6.7.0 v6.6.0 v6.5.0 v6.4.0 CUSTOMIZE YOUR OWN LOGGING When a value is passed to the reporter, an object called observer can be optionally attached. In this case, the name of the observer is added as the prefix of the value name. The observer name should be registered beforehand. Using reporter.scope, you can select which observation to save the observed values.. There are also a global API chainer.report(), which reports observed values with the CHAINER.FUNCTIONS.UPSAMPLING_2D chainer.functions.upsampling_2d. Upsampling using pooling indices. This function produces an upsampled image using pooling indices. This is the original x before max pooling. These are the outputs from the max pooling operation including the resulting indices that will be used to upsample pooled_x. Note that the indices all point to thelargest
CHAINER.OPTIMIZERS.ADAM chainer.optimizers.Adam¶ class chainer.optimizers.Adam (alpha = 0.001, beta1 = 0.9, beta2 = 0.999, eps = 1e-08, eta = 1.0, weight_decay_rate = 0, amsgrad = False, adabound = False, final_lr = 0.1, gamma = 0.001) ¶. Adam optimizer. See: Adam: A Method for Stochastic Optimization Modified for proper weight decay (also called AdamW).AdamW introduces the additional parameters eta and VARIABLES AND DERIVATIVES Variable also supports higher-order derivatives (a.k.a. double backpropagation). Let’s see a simple example. First calculate the first-order derivative. Note that enable_double_backprop=True is passed to y.backward (). chainer.Variable.grad_var is a Variable for chainer.Variable.grad (which is an ndarray ).CHAINER.LINKS.LSTM
chainer.links.LSTM¶ class chainer.links.LSTM (in_size, out_size = None, lateral_init = None, upward_init = None, bias_init = None, forget_bias_init = None) ¶. Fully-connected LSTM layer. This is a fully-connected LSTM layer as a chain. Unlike the lstm() function, which is defined as a stateless activation function, this chain holds upward and lateral connections as child links. USING GPU(S) IN CHAINER Using GPU (s) in Chainer. In the example code of this tutorial, we assume for simplicity that the following symbols are already imported. import math import numpy as np import chainer from chainer import backend from chainer import backends from chainer.backends import cuda from chainer import Function, FunctionNode, gradient_check, report CHAINER.TRAINING.EXTENSIONS.SNAPSHOT chainer.training.extensions.snapshot ¶. chainer.training.extensions.snapshot. Returns a trainer extension to take snapshots of the trainer. This extension serializes the trainer object and saves it to the output directory. It is used to support resuming the training loop from the saved state. This extension is called once per epoch by default. CHAINER.ITERATORS.SERIALITERATOR chainer.iterators.SerialIterator¶ class chainer.iterators.SerialIterator (dataset, batch_size, repeat = True, shuffle = None, order_sampler = None) ¶. Dataset iterator that serially reads the examples. This is a simple implementation of Iterator that just visits each example in either the order of indexes or a shuffled order.. To avoid unintentional performance degradation,the
WEIGHT INITIALIZERS
Weight Initializers ¶. Weight Initializers. Weight initializers are used to initialize arrays. They destructively modify the content of numpy.ndarray or cupy.ndarray . Typically, weight initializers are passed to Link s to initialize their weights and biases. A weight initializer can be any of the following objects. A Powerful, Flexible, and Intuitive Framework for Neural Networks* Get Started
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BRIDGE THE GAP BETWEEN ALGORITHMS AND IMPLEMENTATIONS OF DEEP LEARNING__
POWERFUL
Chainer supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with littleeffort.
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FLEXIBLE
Chainer supports various network architectures including feed-forward nets, convnets, recurrent nets and recursive nets. It also supports per-batch architectures.__
INTUITIVE
Forward computation can include any control flow statements of Python without lacking the ability of backpropagation. It makes code intuitive and easy to debug.QUICK START
Install Chainer:
pip install chainer Run the MNIST example: wget https://github.com/chainer/chainer/archive/v6.4.0.tar.gz tar xzf v6.4.0.tar.gz python chainer-6.4.0/examples/mnist/train_mnist.py Learn more from the official documentation .EXTENSION LIBRARIES
A library that implements various state-of-the-art deep reinforcementalgorithms.
A collection of tools to train and run neural networks for computervision tasks.
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