예제 #1
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def run_mlp():
    # # define the model layers
    # layer1 = Dense(input_size=784, output_size=1000, activation='rectifier')
    # layer2 = Dense(inputs_hook=(1000, layer1.get_outputs()), output_size=1000, activation='rectifier')
    # classlayer3 = SoftmaxLayer(inputs_hook=(1000, layer2.get_outputs()), output_size=10, out_as_probs=False)
    # # add the layers to the prototype
    # mlp = Prototype(layers=[layer1, layer2, classlayer3])

    # test the new way to automatically fill in inputs_hook for models
    mlp = Prototype()
    mlp.add(Dense(input_size=784, output_size=1000, activation='rectifier', noise='dropout'))
    mlp.add(Dense(output_size=1500, activation='tanh', noise='dropout'))
    mlp.add(SoftmaxLayer(output_size=10))

    mnist = MNIST()

    optimizer = AdaDelta(model=mlp, dataset=mnist, epochs=10)
    optimizer.train()

    test_data, test_labels = mnist.test_inputs, mnist.test_targets
    test_data = test_data[:25]
    test_labels = test_labels[:25]
    # use the run function!
    yhat = mlp.run(test_data)
    print('-------')
    print('Prediction: %s' % str(yhat))
    print('Actual:     %s' % str(test_labels.astype('int32')))
def build_model():
    # add layers one-by-one to a Prototype container to build neural net
    # inputs_hook created automatically by Prototype; thus, no need to specify
    mlp = Prototype()
    mlp.add(BasicLayer(input_size=28*28, output_size=512, activation='rectifier', noise='dropout'))
    mlp.add(BasicLayer(output_size=512, activation='rectifier', noise='dropout'))
    mlp.add(SoftmaxLayer(output_size=10))

    return mlp
예제 #3
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def main():
    # First, let's create a simple feedforward MLP with one hidden layer as a Prototype.
    mlp = Prototype()
    mlp.add(
        BasicLayer(input_size=28 * 28,
                   output_size=1000,
                   activation='rectifier',
                   noise='dropout'))
    mlp.add(SoftmaxLayer(output_size=10))

    # Now, we get to choose what values we want to monitor, and what datasets we would like to monitor on!
    # Each Model (in our case, the Prototype), has a get_monitors method that will return a useful
    # dictionary of {string_name: monitor_theano_expression} for various computations of the model we might
    # care about. By default, this method returns an empty dictionary - it was the model creator's job to
    # include potential monitor values.
    mlp_monitors = mlp.get_monitors()
    mlp_channel = MonitorsChannel(name="error")
    for name, expression in mlp_monitors.items():
        mlp_channel.add(
            Monitor(name=name,
                    expression=expression,
                    train=True,
                    valid=True,
                    test=True))

    # create some monitors for statistics about the hidden and output weights!
    # let's look at the mean, variance, and standard deviation of the weights matrices.
    weights_channel = MonitorsChannel(name="weights")
    hiddens_1 = mlp[0].get_params()[0]
    hiddens1_mean = T.mean(hiddens_1)
    weights_channel.add(
        Monitor(name="hiddens_mean", expression=hiddens1_mean, train=True))

    hiddens_2 = mlp[1].get_params()[0]
    hiddens2_mean = T.mean(hiddens_2)
    weights_channel.add(
        Monitor(name="out_mean", expression=hiddens2_mean, train=True))

    # create our plot object to do live plotting!
    plot = Plot(bokeh_doc_name="Monitor Tutorial",
                monitor_channels=[mlp_channel, weights_channel],
                open_browser=True)

    # use SGD optimizer
    optimizer = SGD(model=mlp,
                    dataset=MNIST(concat_train_valid=False),
                    n_epoch=500,
                    save_frequency=100,
                    batch_size=600,
                    learning_rate=.01,
                    lr_decay=False,
                    momentum=.9,
                    nesterov_momentum=True)

    # train, with the plot!
    optimizer.train(plot=plot)
예제 #4
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def add_list_layers():
    # You can also add lists of layers at a time (or as initialization) to a Prototype! This lets you specify
    # more complex interactions between layers!
    hidden1 = BasicLayer(input_size=28 * 28,
                         output_size=512,
                         activation='rectifier',
                         noise='dropout')

    hidden2 = BasicLayer(inputs_hook=(512, hidden1.get_outputs()),
                         output_size=512,
                         activation='rectifier',
                         noise='dropout')

    class_layer = SoftmaxLayer(inputs_hook=(512, hidden2.get_outputs()),
                               output_size=10)

    mlp = Prototype([hidden1, hidden2, class_layer])
    return mlp
예제 #5
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def sequential_add_layers():
    # This method is to demonstrate adding layers one-by-one to a Prototype container.
    # As you can see, inputs_hook are created automatically by Prototype so we don't need to specify!
    mlp = Prototype()
    mlp.add(
        BasicLayer(input_size=28 * 28,
                   output_size=1000,
                   activation='rectifier',
                   noise='dropout',
                   noise_level=0.5))
    mlp.add(
        BasicLayer(output_size=512,
                   activation='rectifier',
                   noise='dropout',
                   noise_level=0.5))
    mlp.add(SoftmaxLayer(output_size=10))

    return mlp
def create_mlp():
    # define the model layers
    relu_layer1 = BasicLayer(input_size=784, output_size=1000, activation='rectifier')
    relu_layer2 = BasicLayer(inputs_hook=(1000, relu_layer1.get_outputs()), output_size=1000, activation='rectifier')
    class_layer3 = SoftmaxLayer(inputs_hook=(1000, relu_layer2.get_outputs()), output_size=10, out_as_probs=False)
    # add the layers as a Prototype
    mlp = Prototype(layers=[relu_layer1, relu_layer2, class_layer3])

    mnist = MNIST()

    optimizer = AdaDelta(model=mlp, dataset=mnist, n_epoch=20)
    optimizer.train()

    test_data, test_labels = mnist.getSubset(TEST)
    test_data = test_data[:25].eval()
    test_labels = test_labels[:25].eval()

    # use the run function!
    preds = mlp.run(test_data)
    log.info('-------')
    log.info("predicted: %s",str(preds))
    log.info("actual:    %s",str(test_labels.astype('int32')))
예제 #7
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    def _build_computation_graph(self):
        ###################### BUILD NETWORK ##########################
        # whether or not to mirror the input images before feeding them into the network
        if self.flag_datalayer:
            layer_1_input = mirror_images(input=self.x,
                                          image_shape=(self.batch_size, 3, 256, 256),  # bc01 format
                                          cropsize=227,
                                          rand=self.rand,
                                          flag_rand=self.rand_crop)
        else:
            layer_1_input = self.x  # 4D tensor (going to be in bc01 format)

        # Start with 5 convolutional pooling layers
        log.debug("convpool layer 1...")
        convpool_layer1 = ConvPoolLayer(inputs_hook=((self.batch_size, 3, 227, 227), layer_1_input),
                                        filter_shape=(96, 3, 11, 11),
                                        convstride=4,
                                        padsize=0,
                                        group=1,
                                        poolsize=3,
                                        poolstride=2,
                                        bias_init=0.0,
                                        local_response_normalization=True)
        # Add this layer's parameters!
        self.params += convpool_layer1.get_params()

        log.debug("convpool layer 2...")
        convpool_layer2 = ConvPoolLayer(inputs_hook=((self.batch_size, 96, 27, 27, ), convpool_layer1.get_outputs()),
                                        filter_shape=(256, 96, 5, 5),
                                        convstride=1,
                                        padsize=2,
                                        group=2,
                                        poolsize=3,
                                        poolstride=2,
                                        bias_init=0.1,
                                        local_response_normalization=True)
        # Add this layer's parameters!
        self.params += convpool_layer2.get_params()

        log.debug("convpool layer 3...")
        convpool_layer3 = ConvPoolLayer(inputs_hook=((self.batch_size, 256, 13, 13), convpool_layer2.get_outputs()),
                                        filter_shape=(384, 256, 3, 3),
                                        convstride=1,
                                        padsize=1,
                                        group=1,
                                        poolsize=1,
                                        poolstride=0,
                                        bias_init=0.0,
                                        local_response_normalization=False)
        # Add this layer's parameters!
        self.params += convpool_layer3.get_params()

        log.debug("convpool layer 4...")
        convpool_layer4 = ConvPoolLayer(inputs_hook=((self.batch_size, 384, 13, 13), convpool_layer3.get_outputs()),
                                        filter_shape=(384, 384, 3, 3),
                                        convstride=1,
                                        padsize=1,
                                        group=2,
                                        poolsize=1,
                                        poolstride=0,
                                        bias_init=0.1,
                                        local_response_normalization=False)
        # Add this layer's parameters!
        self.params += convpool_layer4.get_params()

        log.debug("convpool layer 5...")
        convpool_layer5 = ConvPoolLayer(inputs_hook=((self.batch_size, 384, 13, 13), convpool_layer4.get_outputs()),
                                        filter_shape=(256, 384, 3, 3),
                                        convstride=1,
                                        padsize=1,
                                        group=2,
                                        poolsize=3,
                                        poolstride=2,
                                        bias_init=0.0,
                                        local_response_normalization=False)
        # Add this layer's parameters!
        self.params += convpool_layer5.get_params()

        # Now onto the fully-connected layers!
        fc_config = {
            'activation': 'rectifier',  # type of activation function to use for output
            'weights_init': 'gaussian',  # either 'gaussian' or 'uniform' - how to initialize weights
            'weights_mean': 0.0,  # mean for gaussian weights init
            'weights_std': 0.005,  # standard deviation for gaussian weights init
            'bias_init': 0.0  # how to initialize the bias parameter
        }
        log.debug("fully connected layer 1 (model layer 6)...")
        # we want to have dropout applied to the training version, but not the test version.
        fc_layer6_input = T.flatten(convpool_layer5.get_outputs(), 2)
        fc_layer6 = BasicLayer(inputs_hook=(9216, fc_layer6_input),
                               output_size=4096,
                               noise='dropout',
                               noise_level=0.5,
                               **fc_config)
        # Add this layer's parameters!
        self.params += fc_layer6.get_params()
        # Add the dropout noise switch
        self.noise_switches += fc_layer6.get_noise_switch()

        log.debug("fully connected layer 2 (model layer 7)...")
        fc_layer7 = BasicLayer(inputs_hook=(4096, fc_layer6.get_outputs()),
                               output_size=4096,
                               noise='dropout',
                               noise_level=0.5,
                               **fc_config)

        # Add this layer's parameters!
        self.params += fc_layer7.get_params()
        # Add the dropout noise switch
        self.noise_switches += fc_layer7.get_noise_switch()

        # last layer is a softmax prediction output layer
        softmax_config = {
            'weights_init': 'gaussian',
            'weights_mean': 0.0,
            'weights_std': 0.005,
            'bias_init': 0.0
        }
        log.debug("softmax classification layer (model layer 8)...")
        softmax_layer8 = SoftmaxLayer(inputs_hook=(4096, fc_layer7.get_outputs()),
                                      output_size=1000,
                                      **softmax_config)

        # Add this layer's parameters!
        self.params += softmax_layer8.get_params()

        # finally the softmax output from the whole thing!
        self.output = softmax_layer8.get_outputs()
        self.targets = softmax_layer8.get_targets()

        #####################
        # Cost and monitors #
        #####################
        self.train_cost = softmax_layer8.negative_log_likelihood()
        cost = softmax_layer8.negative_log_likelihood()
        errors = softmax_layer8.errors()
        train_errors = softmax_layer8.errors()

        self.monitors = OrderedDict([('cost', cost), ('errors', errors), ('dropout_errors', train_errors)])

        #########################
        # Compile the functions #
        #########################
        log.debug("Compiling functions!")
        t = time.time()
        log.debug("f_run...")
        # use the actual argmax from the classification
        self.f_run = function(inputs=[self.x], outputs=softmax_layer8.get_argmax_prediction())
        log.debug("compilation took %s", make_time_units_string(time.time() - t))
예제 #8
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from opendeep.data.standard_datasets.image.mnist import MNIST
from opendeep.optimization.adadelta import AdaDelta

if __name__ == '__main__':
    # set up the logging environment to display outputs (optional)
    # although this is recommended over print statements everywhere
    import logging
    from opendeep.log import config_root_logger
    config_root_logger()
    log = logging.getLogger(__name__)
    log.info("Creating softmax!")

    # grab the MNIST dataset
    mnist = MNIST()
    # create the softmax classifier
    s = SoftmaxLayer(input_size=28 * 28, output_size=10, out_as_probs=False)
    # make an optimizer to train it (AdaDelta is a good default)
    optimizer = AdaDelta(model=s, dataset=mnist, epochs=20)
    # perform training!
    optimizer.train()
    # test it on some images!
    test_data, test_labels = mnist.test_inputs[:25], mnist.test_targets[:25]
    # use the run function!
    preds = s.run(test_data)
    print('-------')
    print(preds)
    print(test_labels.astype('int32'))
    print()
    print()
    del mnist
    del s
예제 #9
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from opendeep.optimization.adadelta import AdaDelta


if __name__ == '__main__':
    # set up the logging environment to display outputs (optional)
    # although this is recommended over print statements everywhere
    import logging
    import opendeep.log.logger as logger
    logger.config_root_logger()
    log = logging.getLogger(__name__)
    log.info("Creating softmax!")

    # grab the MNIST dataset
    mnist = MNIST()
    # create the softmax classifier
    s = SoftmaxLayer(input_size=28 * 28, output_size=10, out_as_probs=False)
    # make an optimizer to train it (AdaDelta is a good default)
    optimizer = AdaDelta(model=s, dataset=mnist, n_epoch=20)
    # perform training!
    optimizer.train()
    # test it on some images!
    test_data = mnist.getDataByIndices(indices=range(25), subset=TEST)
    # use the predict function!
    preds = s.predict(test_data)
    print '-------'
    print preds
    print mnist.getLabelsByIndices(indices=range(25), subset=TEST)
    print
    print
    del mnist
    del s
예제 #10
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from opendeep.optimization.adadelta import AdaDelta


if __name__ == '__main__':
    # set up the logging environment to display outputs (optional)
    # although this is recommended over print statements everywhere
    import logging
    from opendeep.log import config_root_logger
    config_root_logger()
    log = logging.getLogger(__name__)
    log.info("Creating softmax!")

    # grab the MNIST dataset
    mnist = MNIST()
    # create the softmax classifier
    s = SoftmaxLayer(input_size=28 * 28, output_size=10, out_as_probs=False)
    # make an optimizer to train it (AdaDelta is a good default)
    optimizer = AdaDelta(model=s, dataset=mnist, epochs=20)
    # perform training!
    optimizer.train()
    # test it on some images!
    test_data, test_labels = mnist.test_inputs[:25], mnist.test_targets[:25]
    # use the run function!
    preds = s.run(test_data)
    print('-------')
    print(preds)
    print(test_labels.astype('int32'))
    print()
    print()
    del mnist
    del s
예제 #11
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    def _build_computation_graph(self):
        ###################### BUILD NETWORK ##########################
        # whether or not to mirror the input images before feeding them into the network
        if self.flag_datalayer:
            layer_1_input = mirror_images(
                input=self.x,
                image_shape=(self.batch_size, 3, 256, 256),  # bc01 format
                cropsize=227,
                rand=self.rand,
                flag_rand=self.rand_crop)
        else:
            layer_1_input = self.x  # 4D tensor (going to be in bc01 format)

        # Start with 5 convolutional pooling layers
        log.debug("convpool layer 1...")
        convpool_layer1 = ConvPoolLayer(inputs_hook=((self.batch_size, 3, 227,
                                                      227), layer_1_input),
                                        filter_shape=(96, 3, 11, 11),
                                        convstride=4,
                                        padsize=0,
                                        group=1,
                                        poolsize=3,
                                        poolstride=2,
                                        bias_init=0.0,
                                        local_response_normalization=True)
        # Add this layer's parameters!
        self.params += convpool_layer1.get_params()

        log.debug("convpool layer 2...")
        convpool_layer2 = ConvPoolLayer(inputs_hook=((
            self.batch_size,
            96,
            27,
            27,
        ), convpool_layer1.get_outputs()),
                                        filter_shape=(256, 96, 5, 5),
                                        convstride=1,
                                        padsize=2,
                                        group=2,
                                        poolsize=3,
                                        poolstride=2,
                                        bias_init=0.1,
                                        local_response_normalization=True)
        # Add this layer's parameters!
        self.params += convpool_layer2.get_params()

        log.debug("convpool layer 3...")
        convpool_layer3 = ConvPoolLayer(
            inputs_hook=((self.batch_size, 256, 13, 13),
                         convpool_layer2.get_outputs()),
            filter_shape=(384, 256, 3, 3),
            convstride=1,
            padsize=1,
            group=1,
            poolsize=1,
            poolstride=0,
            bias_init=0.0,
            local_response_normalization=False)
        # Add this layer's parameters!
        self.params += convpool_layer3.get_params()

        log.debug("convpool layer 4...")
        convpool_layer4 = ConvPoolLayer(
            inputs_hook=((self.batch_size, 384, 13, 13),
                         convpool_layer3.get_outputs()),
            filter_shape=(384, 384, 3, 3),
            convstride=1,
            padsize=1,
            group=2,
            poolsize=1,
            poolstride=0,
            bias_init=0.1,
            local_response_normalization=False)
        # Add this layer's parameters!
        self.params += convpool_layer4.get_params()

        log.debug("convpool layer 5...")
        convpool_layer5 = ConvPoolLayer(
            inputs_hook=((self.batch_size, 384, 13, 13),
                         convpool_layer4.get_outputs()),
            filter_shape=(256, 384, 3, 3),
            convstride=1,
            padsize=1,
            group=2,
            poolsize=3,
            poolstride=2,
            bias_init=0.0,
            local_response_normalization=False)
        # Add this layer's parameters!
        self.params += convpool_layer5.get_params()

        # Now onto the fully-connected layers!
        fc_config = {
            'activation':
            'rectifier',  # type of activation function to use for output
            'weights_init':
            'gaussian',  # either 'gaussian' or 'uniform' - how to initialize weights
            'weights_mean': 0.0,  # mean for gaussian weights init
            'weights_std':
            0.005,  # standard deviation for gaussian weights init
            'bias_init': 0.0  # how to initialize the bias parameter
        }
        log.debug("fully connected layer 1 (model layer 6)...")
        # we want to have dropout applied to the training version, but not the test version.
        fc_layer6_input = T.flatten(convpool_layer5.get_outputs(), 2)
        fc_layer6 = BasicLayer(inputs_hook=(9216, fc_layer6_input),
                               output_size=4096,
                               noise='dropout',
                               noise_level=0.5,
                               **fc_config)
        # Add this layer's parameters!
        self.params += fc_layer6.get_params()
        # Add the dropout noise switch
        self.noise_switches += fc_layer6.get_noise_switch()

        log.debug("fully connected layer 2 (model layer 7)...")
        fc_layer7 = BasicLayer(inputs_hook=(4096, fc_layer6.get_outputs()),
                               output_size=4096,
                               noise='dropout',
                               noise_level=0.5,
                               **fc_config)

        # Add this layer's parameters!
        self.params += fc_layer7.get_params()
        # Add the dropout noise switch
        self.noise_switches += fc_layer7.get_noise_switch()

        # last layer is a softmax prediction output layer
        softmax_config = {
            'weights_init': 'gaussian',
            'weights_mean': 0.0,
            'weights_std': 0.005,
            'bias_init': 0.0
        }
        log.debug("softmax classification layer (model layer 8)...")
        softmax_layer8 = SoftmaxLayer(inputs_hook=(4096,
                                                   fc_layer7.get_outputs()),
                                      output_size=1000,
                                      **softmax_config)

        # Add this layer's parameters!
        self.params += softmax_layer8.get_params()

        # finally the softmax output from the whole thing!
        self.output = softmax_layer8.get_outputs()
        self.targets = softmax_layer8.get_targets()

        #####################
        # Cost and monitors #
        #####################
        self.train_cost = softmax_layer8.negative_log_likelihood()
        cost = softmax_layer8.negative_log_likelihood()
        errors = softmax_layer8.errors()
        train_errors = softmax_layer8.errors()

        self.monitors = OrderedDict([('cost', cost), ('errors', errors),
                                     ('dropout_errors', train_errors)])

        #########################
        # Compile the functions #
        #########################
        log.debug("Compiling functions!")
        t = time.time()
        log.debug("f_run...")
        # use the actual argmax from the classification
        self.f_run = function(inputs=[self.x],
                              outputs=softmax_layer8.get_argmax_prediction())
        log.debug("compilation took %s",
                  make_time_units_string(time.time() - t))
예제 #12
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    # although this is recommended over print statements everywhere
    import logging
    import opendeep.log.logger as logger
    logger.config_root_logger()
    log = logging.getLogger(__name__)
    log.info("Creating MLP!")

    # grab the MNIST dataset
    mnist = MNIST()
    # create the basic layer
    layer1 = BasicLayer(input_size=28 * 28,
                        output_size=1000,
                        activation='relu')
    # create the softmax classifier
    layer2 = SoftmaxLayer(inputs_hook=(1000, layer1.get_outputs()),
                          output_size=10,
                          out_as_probs=False)
    # create the mlp from the two layers
    mlp = Prototype(layers=[layer1, layer2])
    # make an optimizer to train it (AdaDelta is a good default)
    optimizer = AdaDelta(model=mlp, dataset=mnist, n_epoch=20)
    # perform training!
    optimizer.train()
    # test it on some images!
    test_data, test_labels = mnist.getSubset(subset=TEST)
    test_data = test_data[:25].eval()
    test_labels = test_labels[:25].eval()
    # use the run function!
    preds = mlp.run(test_data)
    print('-------')
    print(preds)