# Load our model
# model = densenet169_model(img_rows=img_rows, img_cols=img_cols, color_type=channel, num_classes=num_classes)
# load keras model
model = NASNetMobile(weights=None, classes=10)
sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd,
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
# Start Fine-tuning
model.fit(
X_train,
Y_train,
batch_size=batch_size,
epochs=nb_epoch,
shuffle=True,
verbose=1,
validation_data=(X_valid, Y_valid),
)
# Make predictions
predictions_valid = model.predict(X_valid,
batch_size=batch_size,
verbose=1)
# Cross-entropy loss score
score = log_loss(Y_valid, predictions_valid)
X_train, X_test, y_train, y_test = train_test_split(input,
y,
test_size=0.1,
random_state=42)
model = NASNetMobile(weights=None, classes=7)
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
time_callback = TimeHistory()
model.fit(X_train,
y_train,
epochs=5,
batch_size=32,
validation_data=(X_test, y_test),
callbacks=[time_callback])
name = 'results/UHCS_NASNetMobile_Weights'
score = model.evaluate(X_test, y_test)
print('Test score:', score[0])
print('Test accuracy:', score[1])
model.save_weights(name + '.h5')
times = time_callback.times
file = open('NASNetMobile.txt', 'w')
file.write('Test score:' + str(score[0]) + '\n')
file.write('Test accuracy:' + str(score[1]) + '\n')
file.write(str(times))
file.close()
# opt = keras.optimizers.Adadelta(lr=1.0, rho=0.95, epsilon=None, decay=0.0)
opt = keras.optimizers.Adamax(lr=0.002,
beta_1=0.9,
beta_2=0.999,
epsilon=None,
decay=0.0)
model.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'])
log_path = '/tmp/tflearn_logs/NASNetMobile_LCZ42_Adadelta'
callback = TensorBoard(log_path)
callback.set_model(model)
model.fit(
x_train,
y_train,
batch_size=1024, # 128,1024
epochs=10,
shuffle="batch",
validation_data=(x_test, y_test))
modelpath = 'NASNetMobile_Adadelta_epochs_10.h5'
model.save(modelpath)
print('Saved trained model at %s ' % modelpath)
# Score trained model.
scores = model.evaluate(x_test, y_test, verbose=1)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])