from keras.callbacks import EarlyStopping
num_feats = len(x_train[0])
model = Sequential()
early_stop = EarlyStopping(monitor='loss',
patience=0,
verbose=1,
min_delta=0.005,
mode='auto')
model.add(Dense(num_feats, activation='relu', input_dim=(num_feats)))
model.add(Dropout(0.50))
model.add(Dense(500, activation='relu', kernel_initializer='uniform'))
model.add(Dropout(0.50))
model.add(Dense(2, kernel_initializer='uniform', activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy',
metrics=['accuracy'],
optimizer='adam')
model.fit(x_train,
y_train,
epochs=25,
verbose=1,
callbacks=[early_stop])
y_pred = model.predict_classes(x_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: %.2f%%" % (accuracy * 100.0))
model.add(Dense(units=2, activation='sigmoid'))
# Compile the Network
# More information on optimizer types:
# https://keras.io/optimizers/
model.compile(optimizer=Adam(lr=0.01), loss='binary_crossentropy', metrics=['accuracy'])
# loss='binary_crossentropy' specifies that your model should optimize the log
# loss for binary classification.
# metrics=['accuracy'] specifies that accuracy should be printed out
# Review NN configuration
model.summary()
History = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, verbose=1)
model.predict_classes(X_test)
#model.predict(X_test)
# Log Loss over time
plt.plot(History.history['loss'])
plt.plot(History.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('Loss')
plt.xlabel('Epochs')
plt.legend(['train', 'test'])
plt.show()
# Model accuracy over time
plt.plot(History.history['acc'])
plt.plot(History.history['val_acc'])
#score=0.52721
#%%
import keras
from keras.layers import Dense, Dropout
from keras.models import Sequential
from keras.optimizers import Adam
from keras.utils import to_categorical
#%%
model = Sequential()
model.add(Dense(200, input_shape=(x_train.shape[1], )))
model.add(Dense(200))
model.add(Dropout(0.2))
model.add(Dense(150))
model.add(Dense(50))
model.add(Dense(6, activation='softmax'))
#%%
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['acc'])
#%%
model.fit(x_train, y_train, epochs=1000)
#%%
prediction = model.predict_classes(x_test)
#%%
y_test = [i.argmax() for i in y_test]
#%%
score = f1_score(y_test, prediction, average='weighted')
print(score)
