Keras学习(一):macOS下安装与实例测试

2017-04-06 11:38:55来源:CSDN作者:yzh201612人点击

操作系统:macOS Sierra 10.12.4

Keras官方文档
Keras中文文档

1.安装

  安装基于TensorFlow的Keras
  查看Keras官方文档->Installation
  已安装TF
  采用pip安装方式Keras

sudo pip install keras

  这里写图片描述

  因系统已有scipy-0.13.0b1,于是自动卸载并准备安装scipy-0.19.0。但卸载操作报错:权限不够

  这里写图片描述

  类似问题在之前更新numpy时出现过,当时系统还未升级到10.12,并且已有两个版本的numpy,python默认使用了旧版本numpy。当时采取的措施是手动删除旧版numpy。其他办法可查看此问答(How can I upgrade numpy?—Stack Overflow)。
  出现此类问题的原因,这里(Mac系统10.11及以上升级numpy、scipy等python包报错解决方案)有解释和解决办法。

  安装成功:
  这里写图片描述

2.实例测试

  使用Keras官方提供的例子,Github代码

  在此采用mnist_cnn.py进行测试

'''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_functionimport kerasfrom keras.datasets import mnistfrom keras.models import Sequentialfrom keras.layers import Dense, Dropout, Flattenfrom keras.layers import Conv2D, MaxPooling2Dfrom keras import backend as Kbatch_size = 128num_classes = 10epochs = 12# input image dimensionsimg_rows, img_cols = 28, 28# 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 = (img_rows, img_cols, 1)x_train = x_train.astype('float32')x_test = x_test.astype('float32')x_train /= 255x_test /= 255print('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 matricesy_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'))model.compile(loss=keras.losses.categorical_crossentropy,              optimizer=keras.optimizers.Adadelta(),              metrics=['accuracy'])model.fit(x_train, y_train,          batch_size=batch_size,          epochs=epochs,          verbose=1,          validation_data=(x_test, y_test))score = model.evaluate(x_test, y_test, verbose=0)print('Test loss:', score[0])print('Test accuracy:', score[1])

  下载原文件,或新建文件并复制以上代码。
  运行后,会自动下载mnist数据集,格式为npz。下载过程过慢且无进度显示,看能不能单独下载再导入。找到mnist.load_data()定义如下:

from ..utils.data_utils import get_fileimport numpy as npdef 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)

  另外官方文档中对mnist数据集导入说明如下:

这里写图片描述
  于是可以单独从https://s3.amazonaws.com/img-datasets/mnist.npz 处下载数据集,然后添加到系统~/.keras/datasets/目录下。

$ cp -i ~/Downloads/mnist.npz ~/.keras/datasets

  再次运行程序,顺利导入并开始训练,如下图:
这里写图片描述

  12个epochs后结果为:
  这里写图片描述

  代码中说到:16 seconds per epoch on a GRID K520 GPU,12轮大概3分钟多。
  但是我用笔记本跑的,没用GPU加速,配置还低,结果一共跑了快一个小时…

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