OneEye Changeset - 7cb01d4080c9

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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@@ @@ -5,10 +5,9 @@ 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
@@ @@ -21,9 +20,10 @@ 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()
@@ @@ -73,9 +73,42 @@ def createCNN(): @@
 	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:
@@ @@ -104,4 +137,4 @@ model.fit_generator( @@
 	callbacks=[tensorboard,checkpoint]
+)
-log.info(model.evaluate(testImages,testLabels))
+print(model.evaluate(testImages,testLabels))

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