models.py

# -*- coding: utf-8 -*-
"""models.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1eAT4BL-xbosfCzdRMv4XmcOFa7dQLvML
"""

from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.layers import Dense, Activation, Permute, Dropout
from tensorflow.keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
from tensorflow.keras.layers import SeparableConv2D, DepthwiseConv2D
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import SpatialDropout2D, LSTM
from tensorflow.keras.regularizers import l1_l2
from tensorflow.keras.layers import Input, Flatten, Reshape, InputLayer
from tensorflow.keras.constraints import max_norm
from tensorflow.keras import backend as K
from tensorflow.keras import regularizers

import numpy as np
from keras.datasets import imdb
from tensorflow.keras.layers import LSTM
from keras.layers import Embedding, Dense, Reshape, Input
#embeddings, dense
from tensorflow.keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential

from keras.layers import Dense, Embedding, LSTM, SpatialDropout1D
from tensorflow.keras.optimizers import Adam

#MODEL1 EEGNET
def EEGNet(nb_classes, Chans=64, Samples=128,
           dropoutRate=0.5, kernLength=64, F1=8,
           D=2, F2=16, norm_rate=0.25, dropoutType='Dropout'):
    """ Keras Implementation of EEGNet
    http://iopscience.iop.org/article/10.1088/1741-2552/aace8c/meta
    Note that this implements the newest version of EEGNet and NOT the earlier
    version (version v1 and v2 on arxiv). We strongly recommend using this
    architecture as it performs much better and has nicer properties than
    our earlier version. For example:
        1. Depthwise Convolutions to learn spatial filters within a
        temporal convolution. The use of the depth_multiplier option maps
        exactly to the number of spatial filters learned within a temporal
        filter. This matches the setup of algorithms like FBCSP which learn
        spatial filters within each filter in a filter-bank. This also limits
        the number of free parameters to fit when compared to a fully-connected
        convolution.
        2. Separable Convolutions to learn how to optimally combine spatial
        filters across temporal bands. Separable Convolutions are Depthwise
        Convolutions followed by (1x1) Pointwise Convolutions.
    While the original paper used Dropout, we found that SpatialDropout2D
    sometimes produced slightly better results for classification of ERP
    signals. However, SpatialDropout2D significantly reduced performance
    on the Oscillatory dataset (SMR, BCI-IV Dataset 2A). We recommend using
    the default Dropout in most cases.
    Assumes the input signal is sampled at 128Hz. If you want to use this model
    for any other sampling rate you will need to modify the lengths of temporal
    kernels and average pooling size in blocks 1 and 2 as needed (double the
    kernel lengths for double the sampling rate, etc). Note that we haven't
    tested the model performance with this rule so this may not work well.
    The model with default parameters gives the EEGNet-8,2 model as discussed
    in the paper. This model should do pretty well in general, although it is
	advised to do some model searching to get optimal performance on your
	particular dataset.
    We set F2 = F1 * D (number of input filters = number of output filters) for
    the SeparableConv2D layer. We haven't extensively tested other values of this
    parameter (say, F2 < F1 * D for compressed learning, and F2 > F1 * D for
    overcomplete). We believe the main parameters to focus on are F1 and D.
    Inputs:
      nb_classes      : int, number of classes to classify
      Chans, Samples  : number of channels and time points in the EEG data
      dropoutRate     : dropout fraction
      kernLength      : length of temporal convolution in first layer. We found
                        that setting this to be half the sampling rate worked
                        well in practice. For the SMR dataset in particular
                        since the data was high-passed at 4Hz we used a kernel
                        length of 32.
      F1, F2          : number of temporal filters (F1) and number of pointwise
                        filters (F2) to learn. Default: F1 = 8, F2 = F1 * D.
      D               : number of spatial filters to learn within each temporal
                        convolution. Default: D = 2
      dropoutType     : Either SpatialDropout2D or Dropout, passed as a string.
    """

    if dropoutType == 'SpatialDropout2D':
        dropoutType = SpatialDropout2D
    elif dropoutType == 'Dropout':
        dropoutType = Dropout
    else:
        raise ValueError('dropoutType must be one of SpatialDropout2D '
                         'or Dropout, passed as a string.')

    input1 = Input(shape=(Chans, Samples, 1))

    ##################################################################
    block1 = Conv2D(F1, (1, kernLength), padding='same',
                    input_shape=(Chans, Samples, 1),
                    use_bias=False)(input1)
    block1 = BatchNormalization()(block1)
    block1 = DepthwiseConv2D((Chans, 1), use_bias=False,
                             depth_multiplier=D,
                             depthwise_constraint=max_norm(1.))(block1)
    block1 = BatchNormalization()(block1)
    block1 = Activation('elu')(block1)
    block1 = AveragePooling2D((1, 4))(block1)
    block1 = dropoutType(dropoutRate)(block1)

    block2 = SeparableConv2D(F2, (1, 16),
                             use_bias=False, padding='same')(block1)
    block2 = BatchNormalization()(block2)
    block2 = Activation('elu')(block2)
    block2 = AveragePooling2D((1, 8))(block2)
    block2 = dropoutType(dropoutRate)(block2)

    flatten = Flatten(name='flatten')(block2)

    dense = Dense(nb_classes, name='dense',
                  kernel_constraint=max_norm(norm_rate))(flatten)
    softmax = Activation('softmax', name='softmax')(dense)

    return Model(inputs=input1, outputs=softmax)

#MODEL2 ShallowConvNet
def square(x):
    return K.square(x)


def log(x):
    return K.log(K.clip(x, min_value=1e-7, max_value=10000))


def ShallowConvNet(nb_classes, Chans, Samples, dropoutRate=0.5, weight_decay=0.1,
                   kernel_length=50, pool_s=150, stride_s=30):
    """ Keras implementation of the Shallow Convolutional Network as described
    in Schirrmeister et. al. (2017), Human Brain Mapping.
    Assumes the input is a 2-second EEG signal sampled at 128Hz. Note that in
    the original paper, they do temporal convolutions of length 25 for EEG
    data sampled at 250Hz. We instead use length 13 since the sampling rate is
    roughly half of the 250Hz which the paper used. The pool_size and stride
    in later layers is also approximately half of what is used in the paper.
    Note that we use the max_norm constraint on all convolutional layers, as
    well as the classification layer. We also change the defaults for the
    BatchNormalization layer. We used this based on a personal communication
    with the original authors.
                     ours        original paper    changed version
    pool_size        1, 35       1, 75             1, 150
    strides          1, 7        1, 15             1, 30
    conv filters     1, 13       1, 25             1, 50
    Note that this implementation has not been verified by the original
    authors. We do note that this implementation reproduces the results in the
    original paper with minor deviations.
    """

    # start the model
    input_main = Input((Chans, Samples, 1))
    block1 = Conv2D(20, (1, kernel_length), kernel_regularizer=regularizers.l2(weight_decay),
                    input_shape=(Chans, Samples, 1),
                    kernel_constraint=max_norm(2., axis=(0, 1, 2)))(input_main)
    block1 = Conv2D(20, (Chans, 1), use_bias=False, kernel_regularizer=regularizers.l2(weight_decay),
                    kernel_constraint=max_norm(2., axis=(0, 1, 2)))(block1)
    block1 = BatchNormalization(epsilon=1e-05, momentum=0.1)(block1)
    block1 = Activation(square)(block1)
    block1 = AveragePooling2D(pool_size=(1, pool_s), strides=(1, stride_s))(block1)
    block1 = Activation(log)(block1)
    block1 = Dropout(dropoutRate)(block1)
    flatten = Flatten()(block1)
    dense = Dense(nb_classes, kernel_constraint=max_norm(0.5))(flatten)
    softmax = Activation('softmax')(dense)

    return Model(inputs=input_main, outputs=softmax)

#MODEL3 DeepConvNet
def DeepConvNet(nb_classes, Chans=64, Samples=256,
                dropoutRate=0.5, weight_decay=1):
    """ Keras implementation of the Deep Convolutional Network as described in
    Schirrmeister et. al. (2017), Human Brain Mapping.
    This implementation assumes the input is a 2-second EEG signal sampled at
    128Hz, as opposed to signals sampled at 250Hz as described in the original
    paper. We also perform temporal convolutions of length (1, 5) as opposed
    to (1, 10) due to this sampling rate difference.
    Note that we use the max_norm constraint on all convolutional layers, as
    well as the classification layer. We also change the defaults for the
    BatchNormalization layer. We used this based on a personal communication
    with the original authors.
                      ours        original paper    changed
    pool_size        1, 2        1, 3
    strides          1, 2        1, 3
    conv filters     1, 5        1, 10
    Note that this implementation has not been verified by the original
    authors.
    """

    # start the model
    input_main = Input((Chans, Samples, 1))
    block1 = Conv2D(25, (1, 5), kernel_regularizer=regularizers.l2(weight_decay),
                    input_shape=(Chans, Samples, 1),
                    kernel_constraint=max_norm(2., axis=(0, 1, 2)))(input_main)
    block1 = Conv2D(25, (Chans, 1), kernel_regularizer=regularizers.l2(weight_decay),
                    kernel_constraint=max_norm(2., axis=(0, 1, 2)))(block1)
    block1 = BatchNormalization(epsilon=1e-05, momentum=0.1)(block1)
    block1 = Activation('elu')(block1)
    block1 = MaxPooling2D(pool_size=(1, 2), strides=(1, 2))(block1)
    block1 = Dropout(dropoutRate)(block1)

    block2 = Conv2D(50, (1, 5), kernel_regularizer=regularizers.l2(weight_decay),
                    kernel_constraint=max_norm(2., axis=(0, 1, 2)))(block1)
    block2 = BatchNormalization(epsilon=1e-05, momentum=0.1)(block2)
    block2 = Activation('elu')(block2)
    block2 = MaxPooling2D(pool_size=(1, 2), strides=(1, 2))(block2)
    block2 = Dropout(dropoutRate)(block2)

    block3 = Conv2D(100, (1, 5), kernel_regularizer=regularizers.l2(weight_decay),
                    kernel_constraint=max_norm(2., axis=(0, 1, 2)))(block2)
    block3 = BatchNormalization(epsilon=1e-05, momentum=0.1)(block3)
    block3 = Activation('elu')(block3)
    block3 = MaxPooling2D(pool_size=(1, 2), strides=(1, 2))(block3)
    block3 = Dropout(dropoutRate)(block3)

    block4 = Conv2D(200, (1, 5), kernel_regularizer=regularizers.l2(weight_decay),
                    kernel_constraint=max_norm(2., axis=(0, 1, 2)))(block3)
    block4 = BatchNormalization(epsilon=1e-05, momentum=0.1)(block4)
    block4 = Activation('elu')(block4)
    block4 = MaxPooling2D(pool_size=(1, 2), strides=(1, 2))(block4)
    block4 = Dropout(dropoutRate)(block4)

    flatten = Flatten()(block4)

    dense = Dense(nb_classes, kernel_constraint=max_norm(0.5))(flatten)
    softmax = Activation('softmax')(dense)

    return Model(inputs=input_main, outputs=softmax)



#MODEL4 CNN-LSTM

def cnnlstm(nb_classes, Chans, Samples, dropoutRate=0.5, weight_decay=1):
  
    input1 = Input(shape=(Chans, Samples, 1))
    block1 = Conv2D(50, (1, 50), padding='same', kernel_regularizer=regularizers.l2(weight_decay),
                    input_shape=(Chans, Samples, 1),
                    use_bias=False)(input1)
    block1 = BatchNormalization()(block1)
    block1 = DepthwiseConv2D((Chans, 1), use_bias=False, kernel_regularizer=regularizers.l2(weight_decay),
                             depth_multiplier=2,
                             depthwise_constraint=max_norm(1.))(block1)
    block1 = BatchNormalization()(block1)
    block1 = Activation('elu')(block1)
    block1 = AveragePooling2D(pool_size=(1, 40), strides=(1, 20))(block1)
    block1 = Dropout(dropoutRate)(block1)

    print('block1: ', block1)
    
   reshape1 = Input(shape=(Chans, Samples, 1))

    lstm1 = LSTM(10, return_sequences=True)(reshape1)
    lstm1 = Dropout(dropoutRate)(lstm1)

    lstm2 = LSTM(10)(lstm1)
    lstm2 = Dropout(dropoutRate)(lstm2)
    dense = Dense(nb_classes, kernel_constraint=max_norm(0.5))(lstm2)
    softmax = Activation('softmax')(dense)
    model = Model(inputs=input1, outputs=softmax)
    model.summary()
    
    return(model)