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#converted for ue4 use from
#https://github.com/tensorflow/docs/blob/master/site/en/tutorials/_index.ipynb
import tensorflow as tf
import unreal_engine as ue
from TFPluginAPI import TFPluginAPI
#additional includes
from tensorflow.python.keras import backend as K #to ensure things work well with multi-threading
import numpy as np #for reshaping input
import operator #used for getting max prediction from 1x10 output array
import random
class MnistTutorial(TFPluginAPI):
#keras stop callback
class StopCallback(tf.keras.callbacks.Callback):
def __init__(self, outer):
self.outer = outer
def on_train_begin(self, logs={}):
self.losses = []
def on_batch_end(self, batch, logs={}):
if(self.outer.shouldStop):
#notify on first call
if not (self.model.stop_training):
ue.log('Early stop called!')
self.model.stop_training = True
else:
if(batch % 5 == 0):
#json convertible types are float64 not float32
logs['acc'] = np.float64(logs['acc'])
logs['loss'] = np.float64(logs['loss'])
self.outer.callEvent('TrainingUpdateEvent', logs, True)
#callback an example image from batch to see the actual data we're training on
if((batch*self.outer.batch_size) % 100 == 0):
index = random.randint(0,self.outer.batch_size)*batch
self.outer.jsonPixels['pixels'] = self.outer.x_train[index].ravel().tolist()
self.outer.callEvent('PixelEvent', self.outer.jsonPixels, True)
#Called when TensorflowComponent sends Json input
def onJsonInput(self, jsonInput):
#build the result object
result = {'prediction':-1}
#If we try to predict before training is complete
if not hasattr(self, 'model'):
ue.log_warning("Warning! No 'model' found, prediction invalid. Did training complete?")
return result
#prepare the input, reshape 784 array to a 1x28x28 array
x_raw = jsonInput['pixels']
x = np.reshape(x_raw, (1, 28, 28))
#run the input through our network using stored model and graph
with self.graph.as_default():
output = self.model.predict(x)
#convert output array to max value prediction index (0-10)
index, value = max(enumerate(output[0]), key=operator.itemgetter(1))
#Optionally log the output so you can see the weights for each value and final prediction
ue.log('Output array: ' + str(output) + ',\nPrediction: ' + str(index))
result['prediction'] = index
return result
#Called when TensorflowComponent signals begin training (default: begin play)
def onBeginTraining(self):
ue.log("starting MnistTutorial training")
#training parameters
self.batch_size = 128
num_classes = 10
epochs = 3
#reset the session each time we get training calls
self.kerasCallback = self.StopCallback(self)
K.clear_session()
#load mnist data set
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
#rescale 0-255 -> 0-1.0
x_train, x_test = x_train / 255.0, x_test / 255.0
#define model
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(num_classes, activation=tf.nn.softmax)
])
model.compile( optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
#pre-fill our callEvent data to optimize callbacks
jsonPixels = {}
size = {'x':28, 'y':28}
jsonPixels['size'] = size
self.jsonPixels = jsonPixels
self.x_train = x_train
#this will do the actual training
model.fit(x_train, y_train,
batch_size=self.batch_size,
epochs=epochs,
callbacks=[self.kerasCallback])
model.evaluate(x_test, y_test)
ue.log("Training complete.")
#store our model and graph for prediction
self.graph = tf.get_default_graph()
self.model = model
#required function to get our api
def getApi():
#return CLASSNAME.getInstance()
return MnistTutorial.getInstance()