def network(self, weights=None):
if weights:
model = load_model(weights)
print(model)
else:
model = Sequential()
model.add(Dense(30, activation='relu', input_dim=9))
#model.add(Dense(output_dim=70, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(30, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(30, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(9, activation='sigmoid'))
opt = Adam(self.learning_rate)
model.compile(loss='mse', optimizer=opt)
model._make_predict_function()
return model
class DigitClassifier(object):
batch_size = 128
epochs = 12
layer_1_size = 32
layer_2_size = 64
layer_3_size = 128
first_available_train_samples = 1000
mnist_data = MnistData(0,0)
num_classes = mnist_data.num_classes
x_test = mnist_data.x_test
y_test = mnist_data.y_test
def save(self, path):
with self.graph.as_default():
self.model.save(path)
def load(self, path):
with self.graph.as_default():
self.model = load_model(path)
def build_graph(self):
self.model = Sequential()
self.model.add(Conv2D(DigitClassifier.layer_1_size, kernel_size=(3, 3),
activation='relu',
input_shape=DigitClassifier.mnist_data.input_shape))
self.model.add(Conv2D(DigitClassifier.layer_2_size, (3, 3), activation='relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2)))
self.model.add(Dropout(0.25))
self.model.add(Flatten())
self.model.add(Dense(DigitClassifier.layer_3_size, activation='relu'))
self.model.add(Dropout(0.5))
# self.model.add(Flatten(input_shape=DigitClassifier.mnist_data.input_shape))
self.model.add(Dense(DigitClassifier.num_classes, activation='softmax'))
self.model.compile(loss=categorical_crossentropy, optimizer=Adadelta(), metrics=['accuracy'])
self.model._make_predict_function()
self.graph = tf.get_default_graph()
def predict(self, data):
with self.graph.as_default():
return self.model.predict(data, verbose=0)
def predict_classes(self, data):
return np.argmax(self.predict(data),1)
def evaluate(self):
with self.graph.as_default():
score = self.model.evaluate(DigitClassifier.x_test, DigitClassifier.y_test, verbose=0)
def train(self, x_train, y_train):
with self.graph.as_default():
self.model.fit(x_train, y_train,
batch_size=DigitClassifier.batch_size,
epochs=1,
verbose=0,
validation_data=(DigitClassifier.x_test, DigitClassifier.y_test))
def train_and_return_accuracy_change(self, x_train, y_train):
with self.graph.as_default():
score_before = self.model.evaluate(DigitClassifier.x_test, DigitClassifier.y_test, verbose=0)
score_after = score_before
tempfile = join(gettempdir(), str(uuid4()))
self.save(tempfile)
try:
while True:
self.train(x_train, y_train)
last_score = self.model.evaluate(DigitClassifier.x_test, DigitClassifier.y_test, verbose=0)
if last_score[1] <= score_after[1]:
break
score_after = last_score
finally:
score_after = last_score
if score_before[1] > score_after[1]:
self.load(tempfile)
score_after = score_before
os.remove(tempfile)
return score_before, score_after
