import boundary
import losses
X_train = data.X_train
X_test = data.X_test # CHANGED
Y_train = to_categorical(data.Y_train)
Y_test = to_categorical(data.Y_test) # CHANGED
model = Sequential()
model.add(Dense(100, activation='sigmoid', activity_regularizer=l1(0.0004)))
model.add(Dense(30, activation='sigmoid', activity_regularizer=l1(0.0004)))
model.add(Dense(2, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer=RMSprop(lr=0.001),
metrics=['accuracy'])
history = model.fit(
X_train,
Y_train,
validation_data=(X_test, Y_test), # CHANGED
epochs=30000,
batch_size=25)
boundary.show(
model,
data.X_test,
data.Y_test, # CHANGED
title="Test set")
losses.plot(history)
import echidna as data
import boundary
import losses
X_train = data.X_train
X_validation = data.X_validation
Y_train = to_categorical(data.Y_train)
Y_validation = to_categorical(data.Y_validation)
model = Sequential()
model.add(Dense(100, activation='sigmoid'))
model.add(Dense(30, activation='sigmoid'))
model.add(Dense(2, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer=RMSprop(lr=0.001),
metrics=['accuracy'])
history = model.fit(X_train,
Y_train,
validation_data=(X_validation, Y_validation),
epochs=30000,
batch_size=25)
boundary.show(model, data.X_train, data.Y_train, title="Training set")
boundary.show(model,
data.X_validation,
data.Y_validation,
title="Validation set")
losses.plot(history)
# A three-layered neural network.
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import RMSprop
from keras.utils import to_categorical
import echidna as data
import boundary
X_train = data.X_train
X_validation = data.X_validation
Y_train = to_categorical(data.Y_train)
Y_validation = to_categorical(data.Y_validation)
model = Sequential()
model.add(Dense(100, activation='sigmoid'))
model.add(Dense(2, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer=RMSprop(lr=0.001),
metrics=['accuracy'])
model.fit(X_train,
Y_train,
validation_data=(X_validation, Y_validation),
epochs=30000,
batch_size=25)
boundary.show(model, data.X_train, data.Y_train)