keras可视化pydot graphviz问题

1. 安装

pip install graphviz

pip install pydot

pip install pydot-ng  # 版本兼容需要

# 测试一下
from keras.utils.visualize_util import plot

 

2. 使用:

#!/usr/bin/env python
# coding=utf-8

"""
利用keras cnn进行端到端的验证码识别， 简单直接暴力。
迭代100次可以达到95%的准确率，但是很容易过拟合，泛化能力糟糕， 除了增加训练数据还没想到更好的方法.

__autho__: jkmiao
__email__: miao1202@126.com
___date__:2017-02-08

"""
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Activation, LSTM, Reshape
from keras.layers import Convolution2D, MaxPooling2D
from PIL import Image
import os, random
import numpy as np
from keras.models import model_from_json
from util import CharacterTable
from keras.callbacks import ModelCheckpoint
from sklearn.model_selection import train_test_split
from keras.utils.visualize_util import plot


def load_data(path='img/clearNoise/'):
    fnames = [os.path.join(path, fname) for fname in os.listdir(path) if fname.endswith('jpg')]
    random.shuffle(fnames)
    data, label = [], []
    for fname in fnames:
        imgLabel = fname.split('/')[-1].split('_')[0]
        imgM = np.array(Image.open(fname).convert('L'))
        imgM = 1 * (imgM>180)
        data.append(imgM.reshape((imgM.shape[0], imgM.shape[1], 1)))
        label.append(imgLabel.lower())
    return np.array(data), label

ctable = CharacterTable()
data, label = load_data()
label_onehot = np.zeros((len(label), 216))
for i, lb in enumerate(label):
    label_onehot[i,:] = ctable.encode(lb)
print data.shape
print label_onehot.shape

x_train, x_test, y_train, y_test = train_test_split(data, label_onehot, test_size=0.1)

DEBUG = False

# 建模
if DEBUG:
    model = Sequential()
    model.add(Convolution2D(32, 5, 5, border_mode='valid', input_shape=(60, 200, 1), name='conv1'))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2,2)))
    model.add(Convolution2D(32, 3, 3, name='conv2'))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2,2)))
    model.add(Flatten())
   # model.add(Reshape((20, 60)))
   # model.add(LSTM(32))
    model.add(Dense(512))
    model.add(Activation('relu'))
    model.add(Dense(216))
    model.add(Activation('softmax'))

else:
    model = model_from_json(open('model/ba_cnn_model2.json').read())
    model.load_weights('model/ba_cnn_model2.h5')

# 编译
model.compile(loss='mse', optimizer='adam', metrics=['accuracy'], class_mode='categorical')
model.summary()

# 绘图
plot(model, to_file='model.png', show_shapes=True)

# 训练
check_pointer = ModelCheckpoint('./model/train_len_size1.h5', monitor='val_loss', verbose=1, save_best_only=True)
model.fit(x_train, y_train, batch_size=32, nb_epoch=5, validation_split=0.1, callbacks=[check_pointer])

json_string = model.to_json()
with open('./model/ba_cnn_model2.json', 'w') as fw:
    fw.write(json_string)
model.save_weights('./model/ba_cnn_model2.h5')


# 测试
y_pred = model.predict(x_test, verbose=1)
cnt = 0
for i in range(len(y_pred)):
    guess = ctable.decode(y_pred[i])
    correct = ctable.decode(y_test[i])
    if guess == correct:
        cnt += 1
    if i%10==0:
        print '--'*10, i
        print 'y_pred', guess
        print 'y_test', correct
print cnt/float(len(y_pred))