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model_architectures.py
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model_architectures.py
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import numpy as np
import matplotlib.pyplot as plt
from keras.layers import Input,Dense,Flatten,MaxPool2D,MaxPool1D,Activation,LeakyReLU,LSTM,BatchNormalization,Dropout,Conv2D,Conv1D,Lambda
from keras.constraints import max_norm
from keras.models import Model
from keras.optimizers import Adam
import keras.backend as K
def baseline_lstm():
inputs = Input((128,2,))
l = BatchNormalization()(inputs)
l = LSTM(128,return_sequences=True,activation='tanh',unroll=True)(l)
l = LSTM(128,return_sequences=False,activation='tanh',unroll=True)(l)
l = Dropout(0.2)(l)
outputs = Dense(11,activation='softmax',kernel_constraint = max_norm(2.))(l)
model = Model(inputs,outputs)
model.compile(loss='categorical_crossentropy', optimizer=Adam(learning_rate=0.001) ,metrics=['accuracy'])
model.summary()
return model
def new_lstm():
inputs = Input((128,4,))
l = BatchNormalization()(inputs)
l = LSTM(128,return_sequences=True,activation='tanh',unroll=True)(l)
l = LSTM(128,return_sequences=False,activation='tanh',unroll=True)(l)
l = Dropout(0.2)(l)
outputs = Dense(11,activation='softmax',kernel_constraint = max_norm(2.))(l)
model = Model(inputs,outputs)
model.compile(loss='categorical_crossentropy', optimizer=Adam(learning_rate=0.001) ,metrics=['accuracy'])
model.summary()
return model
def baseline_conv():
inputs = Input((128,2,))
l = BatchNormalization()(inputs)
l = Lambda(lambda t: K.expand_dims(t, -1))(l)
l = Conv2D(filters=256,kernel_size=(3,1),activation='relu')(l)
l = Conv2D(filters=80,kernel_size=(3,2),activation='relu')(l)
l = Flatten()(l)
l = Dense(256,activation='relu')(l)
l = Dropout(0.6)(l)
outputs = Dense(11,activation='softmax',kernel_constraint = max_norm(2.))(l)
model = Model(inputs,outputs)
model.compile(loss='categorical_crossentropy', optimizer=Adam(learning_rate=0.001) ,metrics=['accuracy'])
model.summary()
return model
def base_scrnn():
inputs = Input((128,2,))
l = BatchNormalization()(inputs)
# l = Lambda(lambda t: K.expand_dims(t, -2))(l)
l = Conv1D(filters=128,kernel_size=5,activation='relu')(l)
l = MaxPool1D(3)(l)
l = Conv1D(filters=128,kernel_size=5,activation='relu')(l)
# l = Lambda(lambda t: K.squeeze(t, -2))(l)
l = LSTM(128,return_sequences=True,activation='relu',unroll=True)(l)
l = LSTM(128,return_sequences=True,activation='relu',unroll=True)(l)
l = Dropout(0.8)(l)
l = Flatten()(l)
outputs = Dense(11,activation='softmax',kernel_constraint = max_norm(2.))(l)
model = Model(inputs,outputs)
model.compile(loss='categorical_crossentropy', optimizer=Adam(learning_rate=0.001) ,metrics=['accuracy'])
model.summary()
return model
def scrnn_tanh():
inputs = Input((128,2,))
l = BatchNormalization()(inputs)
# l = Lambda(lambda t: K.expand_dims(t, -2))(l)
l = Conv1D(filters=128,kernel_size=5,activation='relu')(l)
l = MaxPool1D(3)(l)
l = Conv1D(filters=128,kernel_size=5,activation='relu')(l)
# l = Lambda(lambda t: K.squeeze(t, -2))(l)
l = LSTM(128,return_sequences=True,activation='tanh',unroll=True)(l)
l = LSTM(128,return_sequences=True,activation='tanh',unroll=True)(l)
l = Dropout(0.5)(l)
l = Flatten()(l)
outputs = Dense(11,activation='softmax',kernel_constraint = max_norm(2.))(l)
model = Model(inputs,outputs)
model.compile(loss='categorical_crossentropy', optimizer=Adam(learning_rate=0.001) ,metrics=['accuracy'])
model.summary()
return model
def new_scrnn():
inputs = Input((128,4,))
l = BatchNormalization()(inputs)
l = Conv1D(filters=128,kernel_size=5,activation='relu')(l)
l = MaxPool1D(3)(l)
l = Conv1D(filters=128,kernel_size=5,activation='relu')(l)
l = LSTM(128,return_sequences=True,activation='tanh',unroll=True)(l)
l = LSTM(128,return_sequences=True,activation='tanh',unroll=True)(l)
l = Dropout(0.5)(l)
l = Flatten()(l)
outputs = Dense(11,activation='softmax',kernel_constraint = max_norm(2.))(l)
model = Model(inputs,outputs)
model.compile(loss='categorical_crossentropy', optimizer=Adam(learning_rate=0.001) ,metrics=['accuracy'])
model.summary()
return model