kerasのこまごましたノート
4636 ワード
1、kerasカスタム目標損失関数
from keras import backend as K
def my_loss(y_true,y_pred):
return K.mean((y_pred-y_true),axis = -1)
model.compile(loss=my_loss,
optimizer='SGD',
metrics=['accuracy'])
2、kerasはacc-loss曲線を描く
LossHistoryクラスを定義し、lossとaccを保存します.
import keras
import matplotlib.pyplot as plt
class LossHistory(keras.callbacks.Callback):
def on_train_begin(self, logs={}):
self.losses = {'batch':[], 'epoch':[]}
self.accuracy = {'batch':[], 'epoch':[]}
self.val_loss = {'batch':[], 'epoch':[]}
self.val_acc = {'batch':[], 'epoch':[]}
def on_batch_end(self, batch, logs={}):
self.losses['batch'].append(logs.get('loss'))
self.accuracy['batch'].append(logs.get('acc'))
self.val_loss['batch'].append(logs.get('val_loss'))
self.val_acc['batch'].append(logs.get('val_acc'))
def on_epoch_end(self, batch, logs={}):
self.losses['epoch'].append(logs.get('loss'))
self.accuracy['epoch'].append(logs.get('acc'))
self.val_loss['epoch'].append(logs.get('val_loss'))
self.val_acc['epoch'].append(logs.get('val_acc'))
def loss_plot(self, loss_type):
iters = range(len(self.losses[loss_type]))
plt.figure()
# acc
plt.plot(iters, self.accuracy[loss_type], 'r', label='train acc')
# loss
plt.plot(iters, self.losses[loss_type], 'g', label='train loss')
if loss_type == 'epoch':
# val_acc
plt.plot(iters, self.val_acc[loss_type], 'b', label='val acc')
# val_loss
plt.plot(iters, self.val_loss[loss_type], 'k', label='val loss')
plt.grid(True)
plt.xlabel(loss_type)
plt.ylabel('acc-loss')
plt.legend(loc="upper right")
plt.show()
ドラフトの呼び出し
history = LossHistory()
model.fit(···callbacks[history])
history.loss_plot('epoch')
3、mnistデータセットによる読み取り
keras 2.0 mnistなどのデータセットのソースコードをやり直し、mnistを例に
#keras2.0 mnist.py
from ..utils.data_utils import get_file
import numpy as np
def load_data(path='mnist.npz'):
"""Loads the MNIST dataset.
# Arguments
path: path where to cache the dataset locally
(relative to ~/.keras/datasets).
# Returns
Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
"""
path = get_file(path, origin='https://s3.amazonaws.com/img-datasets/mnist.npz')
f = np.load(path)
x_train = f['x_train']
y_train = f['y_train']
x_test = f['x_test']
y_test = f['y_test']
f.close()
return (x_train, y_train), (x_test, y_test)
keras 1を使用する必要がある場合.2バージョン、すなわちload_を再定義data関数
import gzip
from keras.utils.data_utils import get_file
from six.moves import cPickle
import sys
def load_data(path='mnist.pkl.gz'):
"""Loads the MNIST dataset.
# Arguments
path: path where to cache the dataset locally
(relative to ~/.keras/datasets).
# Returns
Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
"""
path = get_file(path, origin='https://s3.amazonaws.com/img-datasets/mnist.pkl.gz')
if path.endswith('.gz'):
f = gzip.open(path, 'rb')
else:
f = open(path, 'rb')
if sys.version_info < (3,):
data = cPickle.load(f)
else:
data = cPickle.load(f, encoding='bytes')
f.close()
return data # (x_train, y_train), (x_test, y_test)
4、運行時間計算関数
import time
class ElapsedTimer(object):
def __init__(self):
self.start_time = time.time()
def elapsed(self,sec):
if sec < 60:
return str(sec) + " sec"
elif sec < (60 * 60):
return str(sec / 60) + " min"
else:
return str(sec / (60 * 60)) + " hr"
def elapsed_time(self):
print("Elapsed: %s " % self.elapsed(time.time() - self.start_time) )
呼び出し:
timer = ElapsedTimer()
model.fit/model.training
timer.elapsed_time()