class Autoencoder_v2(BaseMethod):
def __init__(self, settingsDict, dataset, networkConfig={}):
"""Autoencoder test method. This implementation runs about 5-10% slower than the base Autoencoding method."""
self.HPs.update({
"LearningRate": 0.00005,
"Optimizer": "Adam",
"Epochs": 10,
"LatentSize": 64,
"BatchSize": 64,
"Shuffle": True,
})
self.requiredParams.Append([
"NetworkConfig",
])
super().__init__(settingsDict)
#Processing Other inputs
self.opt = GetOptimizer(self.HPs["Optimizer"],
self.HPs["LearningRate"])
networkConfig.update(dataset.outputSpec)
networkConfig.update({"LatentSize": self.HPs["LatentSize"]})
self.Model = CreateModel(self.HPs["NetworkConfig"],
dataset.inputSpec,
variables=networkConfig,
printSummary=True)
self.Model.compile(optimizer=self.opt, loss=["mse"], metrics=[])
def Train(self, data, callbacks=[]):
self.InitializeCallbacks(callbacks)
self.Model.fit(data["image"],
data["image"],
epochs=self.HPs["Epochs"],
batch_size=self.HPs["BatchSize"],
shuffle=self.HPs["Shuffle"],
callbacks=self.callbacks)
self.SaveModel("models/TestAE")
def ImagesFromImage(self, testImages):
return self.Model.predict({"image": testImages})["Decoder"]
def AnomalyScore(self, testImages):
return tf.reduce_sum(
(testImages - self.ImagesFromImage(testImages))**2,
axis=list(range(1, len(testImages.shape))))
class Classifier(BaseMethod):
def __init__(self, settingsDict, dataset, networkConfig={}):
"""Initializing Model and all Hyperparameters """
self.HPs = {
"LearningRate": 0.00005,
"Optimizer": "Adam",
"Epochs": 10,
"BatchSize": 64,
"Shuffle": True,
}
self.requiredParams = [
"NetworkConfig",
]
#Chacking Valid hyperparameters are specified
CheckFilled(self.requiredParams, settingsDict["NetworkHPs"])
self.HPs.update(settingsDict["NetworkHPs"])
#Processing Other inputs
self.opt = GetOptimizer(self.HPs["Optimizer"],
self.HPs["LearningRate"])
networkConfig.update(dataset.outputSpec)
self.Model = CreateModel(self.HPs["NetworkConfig"],
dataset.inputSpec,
variables=networkConfig)
self.Model.compile(optimizer=self.opt,
loss=["mse"],
metrics=['accuracy'])
self.Model.summary(print_fn=log.info)
self.LoadModel({"modelPath": "models/Test"})
def Train(self, data, callbacks=[]):
self.Model.fit(data["x_train"],
data["y_train"],
epochs=self.HPs["Epochs"],
batch_size=self.HPs["BatchSize"],
shuffle=self.HPs["Shuffle"],
callbacks=callbacks)
self.SaveModel("models/Test")
def Test(self, data):
count = 0
for i in range(0, data["x_test"].shape[0], self.HPs["BatchSize"]):
if i + self.HPs["BatchSize"] > data["x_test"].shape[0]:
pred = self.Model(np.expand_dims(data["x_test"][i:, :, :], -1))
realFinal = tf.math.argmax(data["y_test"][i:, :], axis=-1)
else:
pred = self.Model(
np.expand_dims(
data["x_test"][i:i + self.HPs["BatchSize"], :, :], -1))
realFinal = tf.math.argmax(
data["y_test"][i:i + self.HPs["BatchSize"], :], axis=-1)
predFinal = tf.math.argmax(pred['Classifier'], axis=-1)
count += tf.reduce_sum(
tf.cast(tf.math.equal(realFinal, predFinal), dtype=tf.float32))
print(count, data["x_test"].shape[0])
print(count / data["x_test"].shape[0])