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test.py
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test.py
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'''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''
from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
from iter import *
import numpy as np
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
from importlib import import_module
config =tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction=0.8
set_session(tf.Session(config=config))
batch_size = 500
num_classes = 2
epochs = 20
# input image dimensions
img_rows, img_cols = 33, 33
# the data, shuffled and split between train and test sets
#(x_train, y_train), (x_test, y_test) = mnist.load_data()
#if K.image_data_format() == 'channels_first':
# x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
# x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
# input_shape = (1, img_rows, img_cols)
#else:
#x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
#x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (3,img_rows, img_cols)
#x_train = x_train.astype('float32')
#x_test = x_test.astype('float32')
#x_train /= 255
#x_test /= 255
#print('x_train shape:', x_train.shape)
#print(x_train.shape[0], 'train samples')
#print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
#y_train = keras.utils.to_categorical(y_train, num_classes)
#y_test = keras.utils.to_categorical(y_test, num_classes)
"""model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))"""
net=import_module('symbols.'+"vgg")
model=net.get_symbol(input_shape,num_classes)
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
#ab=int(len(x_train)/10)
train=wkiter("root",istrain=1,fstart=0,friend=15,batch_size=batch_size,end=1)
test=wkiter("root",fstart=15,friend=5,batch_size=batch_size*10,end=1)
print ("train",train.totalnum(),"eval",test.totalnum())
for i in range(epochs):
print("epoch",i)
checkpoint=keras.callbacks.ModelCheckpoint(filepath='save/fullydijetsame_'+str(i),monitor='val_loss',verbose=0,save_best_only=False,mode='auto')
model.fit_generator(train.next(),steps_per_epoch=train.totalnum(),validation_data=test.next(),validation_steps=test.totalnum(),epochs=1,verbose=1,callbacks=[checkpoint])
train.reset()
test.reset()
"""while True:
X_train,Y_train=train.next()
#X_train=x_train[j*ab:(j+1)*ab]
#Y_train=y_train[j*ab:(j+1)*ab]
#print(type(X_train),type(Y_train.shape))
#print(X_train.shape,Y_train.shape)
if(train.endfile==1):
model.fit(X_train, Y_train,
epochs=1,
verbose=1,
validation_data=(test.next())
)
test.reset()
train.reset()
break
else:
model.fit(X_train, Y_train,
epochs=1,
verbose=0,
)"""
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
"""while True:
i+=1
a,b=train.next()
print(a.shape,b.shape,i)
if(train.endfile==1):break"""