"""
Trialwise Decoding on BCIC IV 2a Dataset
========================================

This tutorial shows you how to train and test deep learning models with
Braindecode in a classical EEG setting: you have trials of data with
labels (e.g., Right Hand, Left Hand, etc.).

"""


######################################################################
# Loading and preprocessing the dataset
# -------------------------------------
#


######################################################################
# Loading
# ~~~~~~~
#


######################################################################
# First, we load the data. In this tutorial, we use the functionality of
# braindecode to load datasets through
# `MOABB <https://github.com/NeuroTechX/moabb>`__ to load the BCI
# Competition IV 2a data.
#
# .. note::
#    To load your own datasets either via mne or from
#    preprocessed X/y numpy arrays, see `MNE Dataset
#    Tutorial <./plot_mne_dataset_example.html>`__ and `Numpy Dataset
#    Tutorial <./plot_custom_dataset_example.html>`__.
#

from braindecode.datasets.moabb import MOABBDataset
import mne


def exp(subject_id):
    dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[subject_id])


    ######################################################################
    # Preprocessing
    # ~~~~~~~~~~~~~
    #


    ######################################################################
    # Now we apply preprocessing like bandpass filtering to our dataset. You
    # can either apply functions provided by
    # `mne.Raw <https://mne.tools/stable/generated/mne.io.Raw.html>`__ or
    # `mne.Epochs <https://mne.tools/0.11/generated/mne.Epochs.html#mne.Epochs>`__
    # or apply your own functions, either to the MNE object or the underlying
    # numpy array.
    #
    # .. note::
    #    These prepocessings are now directly applied to the loaded
    #    data, and not on-the-fly applied as transformations in
    #    PyTorch-libraries like
    #    `torchvision <https://pytorch.org/docs/stable/torchvision/index.html>`__.
    #

    from braindecode.datautil.preprocess import exponential_moving_standardize
    from braindecode.datautil.preprocess import MNEPreproc, NumpyPreproc, preprocess

    low_cut_hz = 0.  # low cut frequency for filtering
    high_cut_hz = 38.  # high cut frequency for filtering
    # Parameters for exponential moving standardization
    factor_new = 1e-3
    init_block_size = 1000

    preprocessors = [
        # keep only EEG sensors
        MNEPreproc(fn='pick_types', eeg=True, meg=False, stim=False),
        # convert from volt to microvolt, directly modifying the numpy array
        NumpyPreproc(fn=lambda x: x * 1e6),
        # # bandpass filter
        MNEPreproc(fn='filter', l_freq=low_cut_hz, h_freq=high_cut_hz),
        # # exponential moving standardization
        NumpyPreproc(fn=exponential_moving_standardize, factor_new=factor_new,
            init_block_size=init_block_size)
    ]

    # Transform the data
    preprocess(dataset, preprocessors)


    ######################################################################
    # Cut Compute Windows
    # ~~~~~~~~~~~~~~~~~~~
    #


    ######################################################################
    # Now we cut out compute windows, the inputs for the deep networks during
    # training. In the case of trialwise decoding, we just have to decide if
    # we want to cut out some part before and/or after the trial. For this
    # dataset, in our work, it often was beneficial to also cut out 500 ms
    # before the trial.
    #

    import numpy as np
    from braindecode.datautil.windowers import create_windows_from_events

    trial_start_offset_seconds = -0.5
    # Extract sampling frequency, check that they are same in all datasets
    sfreq = dataset.datasets[0].raw.info['sfreq']
    assert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])
    # Calculate the trial start offset in samples.
    trial_start_offset_samples = int(trial_start_offset_seconds * sfreq)

    # Create windows using braindecode function for this. It needs parameters to define how
    # trials should be used.
    # windows_dataset = create_windows_from_events(
    #     dataset,
    #     trial_start_offset_samples=trial_start_offset_samples,
    #     trial_stop_offset_samples=0,
    #     preload=True,
    # )

    #sliding window aug
    windows_dataset = create_windows_from_events(
        dataset,
        trial_start_offset_samples=trial_start_offset_samples,
        trial_stop_offset_samples=0,
        # window_size_samples=1000,
        # window_stride_samples=74,
        drop_last_window=False,
        preload=True,
    )

    ######################################################################
    # Split dataset into train and valid
    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    #


    ######################################################################
    # We can easily split the dataset using additional info stored in the
    # description attribute, in this case ``session`` column. We select
    # ``session_T`` for training and ``session_E`` for validation.
    #

    splitted = windows_dataset.split('session')
    train_set = splitted['session_T']
    valid_set = splitted['session_E']


    ######################################################################
    # Create model
    # ------------
    #


    ######################################################################
    # Now we create the deep learning model! Braindecode comes with some
    # predefined convolutional neural network architectures for raw
    # time-domain EEG. Here, we use the shallow ConvNet model from `Deep
    # learning with convolutional neural networks for EEG decoding and
    # visualization <https://arxiv.org/abs/1703.05051>`__. These models are
    # pure `PyTorch <https://pytorch.org>`__ deep learning models, therefore
    # to use your own model, it just has to be a normal PyTorch
    # `nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__.
    #

    import torch
    from braindecode.util import set_random_seeds
    from braindecode.models import ShallowFBCSPNet,Deep4Net, EEGNetv4, EEGResNet
    from mynetworks import Deep4Net_origin, ConvClfNet, FcClfNet, EEGNet_v2, ShallowNet

    cuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it
    device = 'cuda' if cuda else 'cpu'
    if cuda:
        torch.backends.cudnn.benchmark = True
    seed = 20200220  # random seed to make results reproducible
    # Set random seed to be able to reproduce results
    set_random_seeds(seed=seed, cuda=cuda)

    n_classes=4
    # Extract number of chans and time steps from dataset
    n_chans = train_set[0][0].shape[0]
    input_window_samples = train_set[0][0].shape[1]

    # embedding_net = ShallowNet(4, 22, input_window_samples)
    # model = FcClfNet(embedding_net)




    model = EEGResNet(
        n_chans,
        n_classes,
        input_window_samples=input_window_samples
    )

    # Send model to GPU
    if cuda:
        model.cuda()


    print(model)
    ######################################################################
    # Training
    # --------
    #


    ######################################################################
    # Now we train the network! EEGClassifier is a Braindecode object
    # responsible for managing the training of neural networks. It inherits
    # from skorch.NeuralNetClassifier, so the training logic is the same as in
    # `Skorch <https://skorch.readthedocs.io/en/stable/>`__.
    #


    ######################################################################
    #    **Note**: In this tutorial, we use some default parameters that we
    #    have found to work well for motor decoding, however we strongly
    #    encourage you to perform your own hyperparameter optimization using
    #    cross validation on your training data.
    #

    from skorch.callbacks import LRScheduler
    from skorch.helper import predefined_split

    from braindecode import EEGClassifier
    # # These values we found good for shallow network:
    lr = 0.0625 * 0.01
    weight_decay = 0

    # # For deep4 they should be:
    # lr = 1 * 0.01
    # weight_decay = 0.5 * 0.001

    batch_size = 60
    n_epochs = 200


    clf = EEGClassifier(
        model,
        criterion=torch.nn.NLLLoss,
        optimizer=torch.optim.AdamW,
        train_split=predefined_split(valid_set),  # using valid_set for validation
        optimizer__lr=lr,
        optimizer__weight_decay=weight_decay,
        batch_size=batch_size,
        callbacks=[
            "accuracy", ("lr_scheduler", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),
        ],
        device=device,
    )


    # Model training for a specified number of epochs. `y` is None as it is already supplied
    # in the dataset.
    clf.fit(train_set, y=None, epochs=n_epochs)

    ######################################################################
    # Plot Results
    # ------------
    #


    ######################################################################
    # Now we use the history stored by Skorch throughout training to plot
    # accuracy and loss curves.
    #

    import matplotlib.pyplot as plt
    from matplotlib.lines import Line2D
    import pandas as pd
    # Extract loss and accuracy values for plotting from history object
    results_columns = ['train_loss', 'valid_loss', 'train_accuracy', 'valid_accuracy']
    df = pd.DataFrame(clf.history[:, results_columns], columns=results_columns,
                      index=clf.history[:, 'epoch'])

    # get percent of misclass for better visual comparison to loss
    df = df.assign(train_misclass=100 - 100 * df.train_accuracy,
                   valid_misclass=100 - 100 * df.valid_accuracy)

    plt.style.use('seaborn')
    fig, ax1 = plt.subplots(figsize=(8, 3))
    df.loc[:, ['train_loss', 'valid_loss']].plot(
        ax=ax1, style=['-', ':'], marker='o', color='tab:blue', legend=False, fontsize=14)

    ax1.tick_params(axis='y', labelcolor='tab:blue', labelsize=14)
    ax1.set_ylabel("Loss", color='tab:blue', fontsize=14)

    ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis

    df.loc[:, ['train_misclass', 'valid_misclass']].plot(
        ax=ax2, style=['-', ':'], marker='o', color='tab:red', legend=False)
    ax2.tick_params(axis='y', labelcolor='tab:red', labelsize=14)
    ax2.set_ylabel("Misclassification Rate [%]", color='tab:red', fontsize=14)
    ax2.set_ylim(ax2.get_ylim()[0], 85)  # make some room for legend
    ax1.set_xlabel("Epoch", fontsize=14)

    # where some data has already been plotted to ax
    handles = []
    handles.append(Line2D([0], [0], color='black', linewidth=1, linestyle='-', label='Train'))
    handles.append(Line2D([0], [0], color='black', linewidth=1, linestyle=':', label='Valid'))
    plt.legend(handles, [h.get_label() for h in handles], fontsize=14)
    plt.tight_layout()

    return df

if __name__ == '__main__':
    import pandas as pd
    df_all = pd.DataFrame()
    for id in range(1,10):
        df = exp(id)
        df_all = pd.concat([df_all, df], axis=1)
        df_all.to_csv("EEGnet_trial.csv",mode='w')
"""
Trialwise Decoding on BCIC IV 2a Dataset
========================================

This tutorial shows you how to train and test deep learning models with
Braindecode in a classical EEG setting: you have trials of data with
labels (e.g., Right Hand, Left Hand, etc.).

"""


######################################################################
# Loading and preprocessing the dataset
# -------------------------------------
#


######################################################################
# Loading
# ~~~~~~~
#


######################################################################
# First, we load the data. In this tutorial, we use the functionality of
# braindecode to load datasets through
# `MOABB <https://github.com/NeuroTechX/moabb>`__ to load the BCI
# Competition IV 2a data.
#
# .. note::
#    To load your own datasets either via mne or from
#    preprocessed X/y numpy arrays, see `MNE Dataset
#    Tutorial <./plot_mne_dataset_example.html>`__ and `Numpy Dataset
#    Tutorial <./plot_custom_dataset_example.html>`__.
#

from braindecode.datasets.moabb import MOABBDataset
import mne


def exp(subject_id):
    dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[subject_id])


    ######################################################################
    # Preprocessing
    # ~~~~~~~~~~~~~
    #


    ######################################################################
    # Now we apply preprocessing like bandpass filtering to our dataset. You
    # can either apply functions provided by
    # `mne.Raw <https://mne.tools/stable/generated/mne.io.Raw.html>`__ or
    # `mne.Epochs <https://mne.tools/0.11/generated/mne.Epochs.html#mne.Epochs>`__
    # or apply your own functions, either to the MNE object or the underlying
    # numpy array.
    #
    # .. note::
    #    These prepocessings are now directly applied to the loaded
    #    data, and not on-the-fly applied as transformations in
    #    PyTorch-libraries like
    #    `torchvision <https://pytorch.org/docs/stable/torchvision/index.html>`__.
    #

    from braindecode.datautil.preprocess import exponential_moving_standardize
    from braindecode.datautil.preprocess import MNEPreproc, NumpyPreproc, preprocess

    low_cut_hz = 0.  # low cut frequency for filtering
    high_cut_hz = 38.  # high cut frequency for filtering
    # Parameters for exponential moving standardization
    factor_new = 1e-3
    init_block_size = 1000

    preprocessors = [
        # keep only EEG sensors
        MNEPreproc(fn='pick_types', eeg=True, meg=False, stim=False),
        # convert from volt to microvolt, directly modifying the numpy array
        NumpyPreproc(fn=lambda x: x * 1e6),
        # # bandpass filter
        MNEPreproc(fn='filter', l_freq=low_cut_hz, h_freq=high_cut_hz),
        # # exponential moving standardization
        NumpyPreproc(fn=exponential_moving_standardize, factor_new=factor_new,
            init_block_size=init_block_size)
    ]

    # Transform the data
    preprocess(dataset, preprocessors)


    ######################################################################
    # Cut Compute Windows
    # ~~~~~~~~~~~~~~~~~~~
    #


    ######################################################################
    # Now we cut out compute windows, the inputs for the deep networks during
    # training. In the case of trialwise decoding, we just have to decide if
    # we want to cut out some part before and/or after the trial. For this
    # dataset, in our work, it often was beneficial to also cut out 500 ms
    # before the trial.
    #

    import numpy as np
    from braindecode.datautil.windowers import create_windows_from_events

    trial_start_offset_seconds = -0.5
    # Extract sampling frequency, check that they are same in all datasets
    sfreq = dataset.datasets[0].raw.info['sfreq']
    assert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])
    # Calculate the trial start offset in samples.
    trial_start_offset_samples = int(trial_start_offset_seconds * sfreq)

    # Create windows using braindecode function for this. It needs parameters to define how
    # trials should be used.
    # windows_dataset = create_windows_from_events(
    #     dataset,
    #     trial_start_offset_samples=trial_start_offset_samples,
    #     trial_stop_offset_samples=0,
    #     preload=True,
    # )

    #sliding window aug
    windows_dataset = create_windows_from_events(
        dataset,
        trial_start_offset_samples=trial_start_offset_samples,
        trial_stop_offset_samples=0,
        # window_size_samples=1000,
        # window_stride_samples=74,
        drop_last_window=False,
        preload=True,
    )

    ######################################################################
    # Split dataset into train and valid
    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    #


    ######################################################################
    # We can easily split the dataset using additional info stored in the
    # description attribute, in this case ``session`` column. We select
    # ``session_T`` for training and ``session_E`` for validation.
    #

    splitted = windows_dataset.split('session')
    train_set = splitted['session_T']
    valid_set = splitted['session_E']


    ######################################################################
    # Create model
    # ------------
    #


    ######################################################################
    # Now we create the deep learning model! Braindecode comes with some
    # predefined convolutional neural network architectures for raw
    # time-domain EEG. Here, we use the shallow ConvNet model from `Deep
    # learning with convolutional neural networks for EEG decoding and
    # visualization <https://arxiv.org/abs/1703.05051>`__. These models are
    # pure `PyTorch <https://pytorch.org>`__ deep learning models, therefore
    # to use your own model, it just has to be a normal PyTorch
    # `nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__.
    #

    import torch
    from braindecode.util import set_random_seeds
    from braindecode.models import ShallowFBCSPNet,Deep4Net, EEGNetv4, EEGResNet
    from mynetworks import Deep4Net_origin, ConvClfNet, FcClfNet, EEGNet_v2, ShallowNet

    cuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it
    device = 'cuda' if cuda else 'cpu'
    if cuda:
        torch.backends.cudnn.benchmark = True
    seed = 20200220  # random seed to make results reproducible
    # Set random seed to be able to reproduce results
    set_random_seeds(seed=seed, cuda=cuda)

    n_classes=4
    # Extract number of chans and time steps from dataset
    n_chans = train_set[0][0].shape[0]
    input_window_samples = train_set[0][0].shape[1]

    # embedding_net = ShallowNet(4, 22, input_window_samples)
    # model = FcClfNet(embedding_net)




    model = EEGResNet(
        n_chans,
        n_classes,
        input_window_samples=input_window_samples
    )

    # Send model to GPU
    if cuda:
        model.cuda()


    print(model)
    ######################################################################
    # Training
    # --------
    #


    ######################################################################
    # Now we train the network! EEGClassifier is a Braindecode object
    # responsible for managing the training of neural networks. It inherits
    # from skorch.NeuralNetClassifier, so the training logic is the same as in
    # `Skorch <https://skorch.readthedocs.io/en/stable/>`__.
    #


    ######################################################################
    #    **Note**: In this tutorial, we use some default parameters that we
    #    have found to work well for motor decoding, however we strongly
    #    encourage you to perform your own hyperparameter optimization using
    #    cross validation on your training data.
    #

    from skorch.callbacks import LRScheduler
    from skorch.helper import predefined_split

    from braindecode import EEGClassifier
    # # These values we found good for shallow network:
    lr = 0.0625 * 0.01
    weight_decay = 0

    # # For deep4 they should be:
    # lr = 1 * 0.01
    # weight_decay = 0.5 * 0.001

    batch_size = 60
    n_epochs = 200


    clf = EEGClassifier(
        model,
        criterion=torch.nn.NLLLoss,
        optimizer=torch.optim.AdamW,
        train_split=predefined_split(valid_set),  # using valid_set for validation
        optimizer__lr=lr,
        optimizer__weight_decay=weight_decay,
        batch_size=batch_size,
        callbacks=[
            "accuracy", ("lr_scheduler", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),
        ],
        device=device,
    )


    # Model training for a specified number of epochs. `y` is None as it is already supplied
    # in the dataset.
    clf.fit(train_set, y=None, epochs=n_epochs)

    ######################################################################
    # Plot Results
    # ------------
    #


    ######################################################################
    # Now we use the history stored by Skorch throughout training to plot
    # accuracy and loss curves.
    #

    import matplotlib.pyplot as plt
    from matplotlib.lines import Line2D
    import pandas as pd
    # Extract loss and accuracy values for plotting from history object
    results_columns = ['train_loss', 'valid_loss', 'train_accuracy', 'valid_accuracy']
    df = pd.DataFrame(clf.history[:, results_columns], columns=results_columns,
                      index=clf.history[:, 'epoch'])

    # get percent of misclass for better visual comparison to loss
    df = df.assign(train_misclass=100 - 100 * df.train_accuracy,
                   valid_misclass=100 - 100 * df.valid_accuracy)

    plt.style.use('seaborn')
    fig, ax1 = plt.subplots(figsize=(8, 3))
    df.loc[:, ['train_loss', 'valid_loss']].plot(
        ax=ax1, style=['-', ':'], marker='o', color='tab:blue', legend=False, fontsize=14)

    ax1.tick_params(axis='y', labelcolor='tab:blue', labelsize=14)
    ax1.set_ylabel("Loss", color='tab:blue', fontsize=14)

    ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis

    df.loc[:, ['train_misclass', 'valid_misclass']].plot(
        ax=ax2, style=['-', ':'], marker='o', color='tab:red', legend=False)
    ax2.tick_params(axis='y', labelcolor='tab:red', labelsize=14)
    ax2.set_ylabel("Misclassification Rate [%]", color='tab:red', fontsize=14)
    ax2.set_ylim(ax2.get_ylim()[0], 85)  # make some room for legend
    ax1.set_xlabel("Epoch", fontsize=14)

    # where some data has already been plotted to ax
    handles = []
    handles.append(Line2D([0], [0], color='black', linewidth=1, linestyle='-', label='Train'))
    handles.append(Line2D([0], [0], color='black', linewidth=1, linestyle=':', label='Valid'))
    plt.legend(handles, [h.get_label() for h in handles], fontsize=14)
    plt.tight_layout()

    return df

if __name__ == '__main__':
    import pandas as pd
    df_all = pd.DataFrame()
    for id in range(1,10):
        df = exp(id)
        df_all = pd.concat([df_all, df], axis=1)
        df_all.to_csv("EEGnet_trial.csv",mode='w')
Trialwise Decoding on BCIC IV 2a Dataset ========================================  This tutorial shows you how to train and test deep learning models with Braindecode in a classical EEG setting: you have trials of data with labels (e.g., Right Hand, Left Hand, etc.). ###################################################################### # Loading and preprocessing the dataset # ------------------------------------- # ###################################################################### # Loading # ~~~~~~~ # ###################################################################### # First, we load the data. In this tutorial, we use the functionality of # braindecode to load datasets through # `MOABB <https://github.com/NeuroTechX/moabb>`__ to load the BCI # Competition IV 2a data. # # .. note:: #    To load your own datasets either via mne or from #    preprocessed X/y numpy arrays, see `MNE Dataset #    Tutorial <./plot_mne_dataset_example.html>`__ and `Numpy Dataset #    Tutorial <./plot_custom_dataset_example.html>`__. # ###################################################################### # Preprocessing # ~~~~~~~~~~~~~ # ###################################################################### # Now we apply preprocessing like bandpass filtering to our dataset. You # can either apply functions provided by # `mne.Raw <https://mne.tools/stable/generated/mne.io.Raw.html>`__ or # `mne.Epochs <https://mne.tools/0.11/generated/mne.Epochs.html#mne.Epochs>`__ # or apply your own functions, either to the MNE object or the underlying # numpy array. # # .. note:: #    These prepocessings are now directly applied to the loaded #    data, and not on-the-fly applied as transformations in #    PyTorch-libraries like #    `torchvision <https://pytorch.org/docs/stable/torchvision/index.html>`__. # # low cut frequency for filtering # high cut frequency for filtering # Parameters for exponential moving standardization # keep only EEG sensors # convert from volt to microvolt, directly modifying the numpy array # # bandpass filter # # exponential moving standardization # Transform the data ###################################################################### # Cut Compute Windows # ~~~~~~~~~~~~~~~~~~~ # ###################################################################### # Now we cut out compute windows, the inputs for the deep networks during # training. In the case of trialwise decoding, we just have to decide if # we want to cut out some part before and/or after the trial. For this # dataset, in our work, it often was beneficial to also cut out 500 ms # before the trial. # # Extract sampling frequency, check that they are same in all datasets # Calculate the trial start offset in samples. # Create windows using braindecode function for this. It needs parameters to define how # trials should be used. # windows_dataset = create_windows_from_events( #     dataset, #     trial_start_offset_samples=trial_start_offset_samples, #     trial_stop_offset_samples=0, #     preload=True, # ) #sliding window aug # window_size_samples=1000, # window_stride_samples=74, ###################################################################### # Split dataset into train and valid # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # ###################################################################### # We can easily split the dataset using additional info stored in the # description attribute, in this case ``session`` column. We select # ``session_T`` for training and ``session_E`` for validation. # ###################################################################### # Create model # ------------ # ###################################################################### # Now we create the deep learning model! Braindecode comes with some # predefined convolutional neural network architectures for raw # time-domain EEG. Here, we use the shallow ConvNet model from `Deep # learning with convolutional neural networks for EEG decoding and # visualization <https://arxiv.org/abs/1703.05051>`__. These models are # pure `PyTorch <https://pytorch.org>`__ deep learning models, therefore # to use your own model, it just has to be a normal PyTorch # `nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__. # # check if GPU is available, if True chooses to use it # random seed to make results reproducible # Set random seed to be able to reproduce results # Extract number of chans and time steps from dataset # embedding_net = ShallowNet(4, 22, input_window_samples) # model = FcClfNet(embedding_net) # Send model to GPU ###################################################################### # Training # -------- # ###################################################################### # Now we train the network! EEGClassifier is a Braindecode object # responsible for managing the training of neural networks. It inherits # from skorch.NeuralNetClassifier, so the training logic is the same as in # `Skorch <https://skorch.readthedocs.io/en/stable/>`__. # ###################################################################### #    **Note**: In this tutorial, we use some default parameters that we #    have found to work well for motor decoding, however we strongly #    encourage you to perform your own hyperparameter optimization using #    cross validation on your training data. # # # These values we found good for shallow network: # # For deep4 they should be: # lr = 1 * 0.01 # weight_decay = 0.5 * 0.001 # using valid_set for validation # Model training for a specified number of epochs. `y` is None as it is already supplied # in the dataset. ###################################################################### # Plot Results # ------------ # ###################################################################### # Now we use the history stored by Skorch throughout training to plot # accuracy and loss curves. # # Extract loss and accuracy values for plotting from history object # get percent of misclass for better visual comparison to loss # instantiate a second axes that shares the same x-axis # make some room for legend # where some data has already been plotted to ax