Changeset - 7cb01d4080c9
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Laman - 6 years ago 2019-05-30 12:42:29

a hinted neural network (failed)
1 file changed with 39 insertions and 6 deletions:
exp/kerokero/train.py
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exp/kerokero/train.py
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@@ -2,31 +2,31 @@ import os
 
import math
 
from time import time
 
import argparse
 
import logging as log
 

			




	
	
	
		
 
import numpy as np

			




	
	
	
		
 
from keras.layers import Conv2D,Dropout,Dense,Flatten,MaxPooling2D,BatchNormalization,GlobalAveragePooling2D,Reshape

			




	
	
	
		
 
from keras.models import Sequential,load_model

			




	
	
	
		
 
from keras.layers import Conv2D,Dropout,Dense,Flatten,MaxPooling2D,BatchNormalization,GlobalAveragePooling2D,Reshape,concatenate

			




	
	
	
		
 
from keras.models import Sequential,load_model,Model,Input

			




	
	
	
		
 
from keras.callbacks import TensorBoard,ModelCheckpoint

			




	
	
	
		
 
import keras.losses

			




	
	
	
		
 
import keras.metrics

			




	
	
	
		
 

			




	
	
	
		
 
import config as cfg

			




	
	
	
		
 
from k_util import averageDistance,generateData

			




	
	
	
		
 

			




	
	
	
		
 
keras.losses.averageDistance=averageDistance

			




	
	
	
		
 
keras.metrics.averageDistance=averageDistance

			




	
	
	
		
 

			




	
	
	
		
 
parser=argparse.ArgumentParser()

			




	
	
	
		
 
parser.add_argument("data")

			




	
	
	
		
 
parser.add_argument("--load_model")

			




	
	
	
		
 
parser.add_argument("--save_model",default="/tmp/gogo-{0:03}.h5")

			




	
	
	
		
 
parser.add_argument("--load_hints")

			




	
	
	
		
 
parser.add_argument("--log_dir",default="/tmp/tflogs")

			




	
	
	
		
 
parser.add_argument("--epochs",type=int,default=100)

			




	
	
	
		
 
parser.add_argument("--initial_epoch",type=int,default=0)

			




	
	
	
		
 
parser.add_argument("--log_dir",default="/tmp/tflogs")

			




	
	
	
		
 
args=parser.parse_args()

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
	
		
 
def createFullyConnected():

			




	
	
	
		
 
	model=Sequential([

			




	
	
	
		
 
		Flatten(input_shape=(224,224)),

			




	
	
		
 
@@ -70,15 +70,48 @@ def createCNN():

			




	
	
	
		
 
	model.add(Reshape((4,2)))

			




	
	
	
		
 

			




	
	
	
		
 
	model.compile(optimizer="rmsprop",loss=averageDistance,metrics=["mae","accuracy"])

			




	
	
	
		
 
	return model

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
	
		
 
model=createCNN()

			




	
	
	
		
 
def createHinted():

			




	
	
	
		
 
	input=Input((224,224,1))

			




	
	
	
		
 
	base=load_model(args.load_hints)

			




	
	
	
		
 
	for layer in base.layers:

			




	
	
	
		
 
		layer.trainable=False

			




	
	
	
		
 
	hints=base(input)

			




	
	
	
		
 

			




	
	
	
		
 
	x=BatchNormalization()(input)

			




	
	
	
		
 
	x=Conv2D(24,(5,5),padding="same",kernel_initializer="he_normal",activation="relu",input_shape=(224,224,1),data_format="channels_last")(x)

			




	
	
	
		
 
	x=MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid")(x)

			




	
	
	
		
 
	x=Conv2D(36,(5,5),activation="relu")(x)

			




	
	
	
		
 
	x=MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid")(x)

			




	
	
	
		
 
	x=Conv2D(48,(5,5),activation="relu")(x)

			




	
	
	
		
 
	x=MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid")(x)

			




	
	
	
		
 
	x=Conv2D(64,(3,3),activation="relu")(x)

			




	
	
	
		
 
	x=MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid")(x)

			




	
	
	
		
 
	x=Conv2D(64,(3,3),activation="relu")(x)

			




	
	
	
		
 
	x=GlobalAveragePooling2D()(x)

			




	
	
	
		
 

			




	
	
	
		
 
	x=concatenate([x,Flatten()(hints)])

			




	
	
	
		
 
	x=Dense(500,activation="relu")(x)

			




	
	
	
		
 
	x=Dense(90,activation="relu")(x)

			




	
	
	
		
 

			




	
	
	
		
 
	predictions=Reshape((4,2))(Dense(8)(x))

			




	
	
	
		
 

			




	
	
	
		
 
	model=Model(inputs=input,outputs=predictions)

			




	
	
	
		
 

			




	
	
	
		
 
	model.compile(optimizer='rmsprop',loss=averageDistance,metrics=['mae','accuracy'])

			




	
	
	
		
 
	return model

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
	
		
 
if args.load_model:

			




	
	
	
		
 
	model=load_model(args.load_model)

			




	
	
	
		
 
else:

			




	
	
	
		
 
	model=createHinted()

			




	
	
	
		
 
	model.summary()

			




	
	
	
		
 

			




	
	
	
		
 
log.info("loading data...")

			




	
	
	
		
 
with np.load(args.data) as data:

			




	
	
	
		
 
	trainImages=(np.float32(data["trainImages"])/128-1).reshape((-1,224,224,1))

			




	
	
	
		
 
	trainLabels=data["trainLabels"].reshape((-1,4,2))

			




	
	
	
		
 
	testImages=(np.float32(data["testImages"])/128-1).reshape((-1,224,224,1))

			




	
	
		
 
@@ -101,7 +134,7 @@ model.fit_generator(

			




	
	
	
		
 
	steps_per_epoch=math.ceil(n_/20),

			




	
	
	
		
 
	validation_data=generateData(valImages,valLabels,batch_size=20),

			




	
	
	
		
 
	validation_steps=math.ceil(k/20),

			




	
	
	
		
 
	callbacks=[tensorboard,checkpoint]

			




	
	
	
		
 
)

			




	
	
	
		
 

			




	
	
	
		
 
log.info(model.evaluate(testImages,testLabels))

			




	
	
	
		
 
print(model.evaluate(testImages,testLabels))

			




        

    



    


    



    



      

      





    
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