_name = 'mynn'

def build_and_train(self, x, y):

from keras.layers import Dense, Activation, Flatten, Dropout

from keras.models import Sequential

from sklearn.preprocessing import LabelBinarizer

import matplotlib.pyplot as plt

encoder = LabelBinarizer()

onehot_y = encoder.fit_transform(y)

lx,ly,lz = x.shape

self.meta.update({

"word_vec_size": lz,

"len_words": ly,

"encoder_classes": encoder.classes_

})

model = Sequential()

model.add(Dense(120, activation='tanh', input_shape=(ly, lz)))

model.add(Dense(60, activation='tanh', input_shape=(ly, lz)))

model.add(Dropout(0.1))

model.add(Dense(30, activation='tanh', input_shape=(ly, lz)))

model.add(Flatten())

model.add(Dense(onehot_y.shape[1], activation='softmax'))

model.compile('Adam', loss='mse', metrics=['acc'])

history = model.fit(x, onehot_y, batch_size=5, epochs=100, validation_split=0.1)

scores = float(model.test_on_batch(x, onehot_y)[0])

print(f'точность на тестовых данных: {scores*100}')

plt.figure(0)

plt.subplot(2, 1, 1)

plt.plot(history.history['loss'])

plt.plot(history.history['val_loss'], '--')

plt.title('Loss')

plt.legend(['Loss', 'Val loss'])

plt.subplot(2, 1, 2)

plt.title('Acc')

plt.plot(history.history['acc'])

plt.savefig(str(Path(self.output().path).with_suffix('.png')))

_name = 'mynn2'

def build_and_train(self, x, y):

from keras.layers import Dense, Activation, Flatten, Dropout

from keras.models import Sequential

from sklearn.preprocessing import LabelBinarizer

import matplotlib.pyplot as plt

encoder = LabelBinarizer()

onehot_y = encoder.fit_transform(y)

lx,ly,lz = x.shape

self.meta.update({

"word_vec_size": lz,

"len_words": ly,

"encoder_classes": encoder.classes_

})

model = Sequential()

#добавил несколько слоев

model.add(Dense(120, activation='tanh', input_shape=(ly, lz)))

model.add(Dense(90, activation='relu'))

#дальше меняются только функции активации, добавляю дропауты

model.add(Dropout(0.1))

model.add(Dense(60, activation='tanh'))

model.add(Dropout(0.1))

model.add(Flatten())

model.add(Dense(onehot_y.shape[1], activation='softmax'))

model.compile('Adam', loss='mse', metrics=['acc'])

history = model.fit(x, onehot_y, batch_size=5, epochs=100, validation_split=0.1)

scores = float(model.test_on_batch(x, onehot_y)[0])

print(f'точность на тестовых данных: {scores*100}')

plt.figure(0)

plt.subplot(2, 1, 1)

plt.plot(history.history['loss'])

plt.plot(history.history['val_loss'], '--')

plt.title('Loss')

plt.legend(['Loss', 'Val loss'])

plt.subplot(2, 1, 2)

plt.title('Acc')

plt.plot(history.history['acc'])

plt.savefig(str(Path(self.output().path).with_suffix('.png')))

_name = 'mynn3'

def build_and_train(self, x, y):

from keras.layers import Dense, Activation, Dropout, Flatten

from keras.models import Sequential

from sklearn.preprocessing import LabelBinarizer

#from keras.callbacks import ModelCheckpoint

import matplotlib.pyplot as plt

encoder = LabelBinarizer()

onehot_y = encoder.fit_transform(y)

lx,ly,lz = x.shape

self.meta.update({

"word_vec_size": lz,

"len_words": ly,

"encoder_classes": encoder.classes_

})

model = Sequential()

#+drop 0.1

model.add(Dense(60, input_shape=(ly, lz)))

model.add(Dropout(0.1))

#activate fun exp

model.add(Dense(60, activation='relu'))

#drop +0.1

model.add(Dropout(0.2))

model.add(Flatten())

model.add(Dense(onehot_y.shape[1], activation='softmax'))

model.compile('Adam', loss='mse', metrics=['acc'])

#checkpointer = ModelCheckpoint(filepath='C:/Users/dkkay/Desktop/Дипломная/train-nn-dev/dist/checkpoints', verbose=1, save_best_only=True)

history = model.fit(x, onehot_y, batch_size=5, epochs=100, validation_split=0) #callbacks=[checkpointer])

scores = float(model.test_on_batch(x, onehot_y)[0])

print(f'точность на тестовых данных: {scores*100}')

plt.figure(0)

plt.subplot(2, 1, 1)

plt.plot(history.history['loss'])

plt.plot(history.history['val_loss'], '--')

plt.title('Loss')

plt.legend(['Loss', 'Val loss'])

plt.subplot(2, 1, 2)

plt.title('Acc')

plt.plot(history.history['acc'])

plt.savefig(str(Path(self.output().path).with_suffix('.png')))

_name = 'mynn4'

def build_and_train(self, x, y):

from keras.layers import Dense, Activation, Flatten, Dropout

from keras.models import Sequential

from sklearn.preprocessing import LabelBinarizer

import matplotlib.pyplot as plt

encoder = LabelBinarizer()

onehot_y = encoder.fit_transform(y)

lx,ly,lz = x.shape

self.meta.update({

"word_vec_size": lz,

"len_words": ly,

"encoder_classes": encoder.classes_

})

model = Sequential()

#

model.add(Dense(160, input_shape=(ly, lz)))

model.add(Dense(90, activation='tanh'))

#drop 0.3

model.add(Dropout(0.3))

model.add(Dense(60, activation='relu'))

model.add(Flatten())

model.add(Dense(onehot_y.shape[1], activation='softmax'))

model.compile('Adam', loss='mse', metrics=['acc'])

history = model.fit(x, onehot_y, batch_size=5, epochs=100, validation_split=0.1)

scores = float(model.test_on_batch(x, onehot_y)[0])

print(f'точность на тестовых данных: {scores*100}')

plt.figure(0)

plt.subplot(2, 1, 1)

plt.plot(history.history['loss'])

plt.plot(history.history['val_loss'], '--')

plt.title('Loss')

plt.legend(['Loss', 'Val loss'])

plt.subplot(2, 1, 2)

plt.title('Acc')

plt.plot(history.history['acc'])

plt.savefig(str(Path(self.output().path).with_suffix('.png')))

_name = 'mynn5'

def build_and_train(self, x, y):

from keras.layers import Dense, Activation, Dropout, Flatten

from keras.models import Sequential

from sklearn.preprocessing import LabelBinarizer

import matplotlib.pyplot as plt

encoder = LabelBinarizer()

onehot_y = encoder.fit_transform(y)

lx,ly,lz = x.shape

self.meta.update({

"word_vec_size": lz,

"len_words": ly,

"encoder_classes": encoder.classes_

})

model = Sequential()

#используем 2*2 одинаковых слоя, drop -0.1

model.add(Dense(180, input_shape=(ly, lz)))

model.add(Dropout(0.3))

model.add(Flatten())

model.add(Dense(90, activation = "relu"))

model.add(Dropout(0.2))

model.add(Dense(180, activation='exponential'))

model.add(Dropout(0.1))

model.add(Dense(90, activation = "relu"))

model.add(Dense(onehot_y.shape[1], activation='softmax'))

model.compile('Adam', loss='mse', metrics=['acc'])

history = model.fit(x, onehot_y, batch_size=5, epochs=100, validation_split=0.1)

scores = float(model.test_on_batch(x, onehot_y)[0])

print(f'точность на тестовых данных: {scores*100}')

plt.figure(0)

plt.subplot(2, 1, 1)

plt.plot(history.history['loss'])

plt.plot(history.history['val_loss'], '--')

plt.title('Loss')

plt.legend(['Loss', 'Val loss'])

plt.subplot(2, 1, 2)

plt.title('Acc')

plt.plot(history.history['acc'])

plt.savefig(str(Path(self.output().path).with_suffix('.png')))

_name = 'mynn6'

def build_and_train(self, x, y):

from keras.layers import Dense, Activation, Flatten

from keras.models import Sequential

from sklearn.preprocessing import LabelBinarizer

import matplotlib.pyplot as plt

encoder = LabelBinarizer()

onehot_y = encoder.fit_transform(y)

lx,ly,lz = x.shape

self.meta.update({

"word_vec_size": lz,

"len_words": ly,

"encoder_classes": encoder.classes_

})

model = Sequential()

#разные активации

model.add(Dense(100, input_shape=(ly, lz)))

model.add(Dense(50, activation='exponential'))

model.add(Dense(100, activation='linear'))

model.add(Dense(150, activation='softplus'))

model.add(Dense(100, activation='relu'))

model.add(Flatten())

model.add(Dense(onehot_y.shape[1], activation='softmax'))

model.compile('Adam', loss='mse', metrics=['acc'])

history = model.fit(x, onehot_y, batch_size=5, epochs=100, validation_split=0.1)

scores = float(model.test_on_batch(x, onehot_y)[0])

print(f'точность на тестовых данных: {scores*100}')

plt.figure(0)

plt.subplot(2, 1, 1)

plt.plot(history.history['loss'])

plt.plot(history.history['val_loss'], '--')

plt.title('Loss')

plt.legend(['Loss', 'Val loss'])

plt.subplot(2, 1, 2)

plt.title('Acc')

plt.plot(history.history['acc'])

plt.savefig(str(Path(self.output().path).with_suffix('.png')))

_name = 'mynn7'

def build_and_train(self, x, y):

from keras.layers import Dense, Activation, Conv1D, Dropout, Flatten

from keras.models import Sequential

from sklearn.preprocessing import LabelBinarizer

import matplotlib.pyplot as plt

encoder = LabelBinarizer()

onehot_y = encoder.fit_transform(y)

lx,ly,lz = x.shape

self.meta.update({

"word_vec_size": lz,

"len_words": ly,

"encoder_classes": encoder.classes_

})

model = Sequential()

#+ conv1d

model.add(Dense(120, input_shape=(ly, lz)))

model.add(Conv1D(filters=1, kernel_size=3, padding='valid', activation='relu'))

model.add(Dropout(0.2))

model.add(Dense(60, activation='tanh'))

model.add(Flatten())

model.add(Dense(onehot_y.shape[1], activation='softmax'))

model.compile('Adam', loss='mse', metrics=['acc'])

history = model.fit(x, onehot_y, batch_size=5, epochs=100, validation_split=0.1)

scores = float(model.test_on_batch(x, onehot_y)[0])

print(f'точность на тестовых данных: {scores*100}')

plt.figure(0)

plt.subplot(2, 1, 1)

plt.plot(history.history['loss'])

plt.plot(history.history['val_loss'], '--')

plt.title('Loss')

plt.legend(['Loss', 'Val loss'])

plt.subplot(2, 1, 2)

plt.title('Acc')

plt.plot(history.history['acc'])

plt.savefig(str(Path(self.output().path).with_suffix('.png')))

_name = 'mynn8'

def build_and_train(self, x, y):

from keras.layers import Dense, Activation, Dropout, Flatten

from keras.models import Sequential

from sklearn.preprocessing import LabelBinarizer

import matplotlib.pyplot as plt

encoder = LabelBinarizer()

onehot_y = encoder.fit_transform(y)

lx,ly,lz = x.shape

self.meta.update({

"word_vec_size": lz,

"len_words": ly,

"encoder_classes": encoder.classes_

})

model = Sequential()

#много слоев

model.add(Dense(120, input_shape=(ly, lz)))

model.add(Dense(160, activation='tanh'))

model.add(Dropout(0.1))

model.add(Dense(100, activation='sigmoid'))

model.add(Dropout(0.2))

model.add(Dense(60, activation='relu'))

model.add(Dropout(0.3))

model.add(Dense(80, activation='hard_sigmoid'))

model.add(Flatten())

model.add(Dense(onehot_y.shape[1], activation='softmax'))

model.compile('Adam', loss='mse', metrics=['acc'])

history = model.fit(x, onehot_y, batch_size=5, epochs=100, validation_split=0.1)

scores = float(model.test_on_batch(x, onehot_y)[0])

print(f'точность на тестовых данных: {scores*100}')

plt.figure(0)

plt.subplot(2, 1, 1)

plt.plot(history.history['loss'])

plt.plot(history.history['val_loss'], '--')

plt.title('Loss')

plt.legend(['Loss', 'Val loss'])

plt.subplot(2, 1, 2)

plt.title('Acc')

plt.plot(history.history['acc'])

plt.savefig(str(Path(self.output().path).with_suffix('.png')))

_name = 'mynn9'

def build_and_train(self, x, y):

from keras.layers import Dense, Activation, Dropout, Flatten

from keras.models import Sequential

from sklearn.preprocessing import LabelBinarizer

import matplotlib.pyplot as plt

encoder = LabelBinarizer()

onehot_y = encoder.fit_transform(y)

lx,ly,lz = x.shape

self.meta.update({

"word_vec_size": lz,

"len_words": ly,

"encoder_classes": encoder.classes_

})

model = Sequential()

#+layer 'activation', init

model.add(Dense(100, input_shape=(ly, lz)))

model.add(Dropout(0.15))

model.add(Dense(50, init='uniform', activation='sigmoid'))

model.add(Activation('softplus'))

model.add(Dropout(0.15))

model.add(Dense(100, init='uniform', activation='tanh'))

model.add(Flatten())

model.add(Dense(onehot_y.shape[1], activation='softmax'))

model.compile('Adam', loss='mse', metrics=['acc'])

history = model.fit(x, onehot_y, batch_size=5, epochs=100, validation_split=0.1)

scores = float(model.test_on_batch(x, onehot_y)[0])

print(f'точность на тестовых данных: {scores*100}')

plt.figure(0)

plt.subplot(2, 1, 1)

plt.plot(history.history['loss'])

plt.plot(history.history['val_loss'], '--')

plt.title('Loss')

plt.legend(['Loss', 'Val loss'])

plt.subplot(2, 1, 2)

plt.title('Acc')

plt.plot(history.history['acc'])

plt.savefig(str(Path(self.output().path).with_suffix('.png')))

_name = 'mynn10'

def build_and_train(self, x, y):

from keras.layers import Dense, Activation, Flatten

from keras.models import Sequential

from sklearn.preprocessing import LabelBinarizer

import matplotlib.pyplot as plt

encoder = LabelBinarizer()

onehot_y = encoder.fit_transform(y)

lx,ly,lz = x.shape

self.meta.update({

"word_vec_size": lz,

"len_words": ly,

"encoder_classes": encoder.classes_

})

model = Sequential()

#drop?

model.add(Dense(120, input_shape=(ly, lz)))

model.add(Dense(60, input_shape=(ly, lz), activation='relu'))

model.add(Dense(140, input_shape=(ly, lz), activation='tanh'))

model.add(Dense(70, input_shape=(ly, lz),activation='relu'))

model.add(Flatten())

model.add(Dense(onehot_y.shape[1], activation='softmax'))

model.compile('Adam', loss='mse', metrics=['acc'])

history = model.fit(x, onehot_y, batch_size=5, epochs=100, validation_split=0.1)

scores = float(model.test_on_batch(x, onehot_y)[0])

print(f'точность на тестовых данных: {scores*100}')

plt.figure(0)

plt.subplot(2, 1, 1)

plt.plot(history.history['loss'])

plt.plot(history.history['val_loss'], '--')

plt.title('Loss')

plt.legend(['Loss', 'Val loss'])

plt.subplot(2, 1, 2)

plt.title('Acc')

plt.plot(history.history['acc'])

plt.savefig(str(Path(self.output().path).with_suffix('.png')))

_name = 'mynn11'

def build_and_train(self, x, y):

from keras.layers import Dense, Activation, Flatten, Dropout

from keras.models import Sequential

from sklearn.preprocessing import LabelBinarizer

import matplotlib.pyplot as plt

encoder = LabelBinarizer()

onehot_y = encoder.fit_transform(y)

lx,ly,lz = x.shape

self.meta.update({

"word_vec_size": lz,

"len_words": ly,

"encoder_classes": encoder.classes_

})

model = Sequential()

model.add(Dense(120, activation='tanh', input_shape=(ly, lz)))

model.add(Dense(60, activation='tanh', input_shape=(ly, lz)))

model.add(Dropout(0.1))

model.add(Dense(30, activation='tanh', input_shape=(ly, lz)))

model.add(Flatten())

model.add(Dense(onehot_y.shape[1], activation='softmax'))

model.compile('Adam', loss='mse', metrics=['acc'])

history = model.fit(x, onehot_y, batch_size=5, epochs=100, validation_split=0.1)

scores = float(model.test_on_batch(x, onehot_y)[0])

print(f'точность на тестовых данных: {scores*100}')

plt.figure(0)

plt.subplot(2, 1, 1)

plt.plot(history.history['loss'])

plt.plot(history.history['val_loss'], '--')

plt.title('Loss')

plt.legend(['Loss', 'Val loss'])

plt.subplot(2, 1, 2)

plt.title('Acc')

plt.plot(history.history['acc'])

plt.savefig(str(Path(self.output().path).with_suffix('.png')))

return model