1、keras自定义目标损失函数

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绘制acc-loss曲线

定义一个LossHistory类，保存loss和acc

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()

调用绘制

history = LossHistory()
model.fit(···callbacks[history])
history.loss_plot('epoch')

3、基于mnist数据集的读取

由于keras2.0重做了mnist等数据集的源代码，以mnist为例

#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)

若仍需使用keras1.2版本，即重新定义load_data函数

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、运行时间计算函数

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) )

调用：

timer = ElapsedTimer()
model.fit/model.training
timer.elapsed_time()