keras_cnn.py 訓練及建模
#!/usr/bin/env python # coding=utf-8 """ 利用keras cnn進行端到端的驗證碼識別， 簡單直接暴力。 迭代100次可以達到95%的准確率，但是很容易過擬合，泛化能力糟糕， 除了增加訓練數據還沒想到更好的方法. __autho__: jkmiao __email__: miao1202@126.com ___date__:2017-02-08 """ from keras.models import Model from keras.layers import Dense, Dropout, Flatten, Input, merge from keras.layers import Convolution2D, MaxPooling2D from keras.preprocessing.image import ImageDataGenerator 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 EarlyStopping 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 i, fname in enumerate(fnames): imgLabel = fname.split('/')[-1].split('_')[0] if len(imgLabel)!=6: print 'error: ', fname continue imgM = np.array(Image.open(fname).convert('L')) imgM = 1 * (imgM>180) data.append(imgM.reshape((60, 200, 1))) label.append(imgLabel.lower()) return np.array(data), label ctable = CharacterTable() data, label = load_data() print data[0].max(), data[0].min() label_onehot = np.zeros((len(label), 216)) for i, lb in enumerate(label): label_onehot[i,:] = ctable.encode(lb) print data.shape, data[-1].max(), data[-1].min() print label_onehot.shape datagen = ImageDataGenerator(shear_range=0.08, zoom_range=0.08, horizontal_flip=False, rotation_range=5, width_shift_range=0.06, height_shift_range=0.06) datagen.fit(data) x_train, x_test, y_train, y_test = train_test_split(data, label_onehot, test_size=0.1) DEBUG = False # 建模 if DEBUG: input_img = Input(shape=(60, 200, 1)) inner = Convolution2D(16, 7, 7, border_mode='same', activation='relu')(input_img) inner = MaxPooling2D(pool_size=(2,2))(inner) inner = Convolution2D(16, 3, 3, border_mode='same')(inner) inner = Convolution2D(16, 3, 3, border_mode='same')(inner) inner = MaxPooling2D(pool_size=(2,2))(inner) inner = Convolution2D(16, 3, 3, border_mode='same')(inner) encoder_a = Flatten()(inner) inner = Convolution2D(16, 5, 5, border_mode='same', activation='relu')(input_img) inner = MaxPooling2D(pool_size=(2,2))(inner) inner = Convolution2D(16, 3, 3, border_mode='same')(inner) inner = Convolution2D(16, 3, 3, border_mode='same')(inner) inner = MaxPooling2D(pool_size=(2,2))(inner) inner = Convolution2D(16, 3, 3, border_mode='same')(inner) encoder_b = Flatten()(inner) inner = Convolution2D(16, 3, 3, border_mode='same', activation='relu')(input_img) inner = MaxPooling2D(pool_size=(2,2))(inner) inner = Convolution2D(16, 3, 3, border_mode='same')(inner) inner = Convolution2D(16, 3, 3, border_mode='same')(inner) inner = MaxPooling2D(pool_size=(2,2))(inner) inner = Convolution2D(16, 3, 3, border_mode='same')(inner) encoder_c = Flatten()(inner) input = merge([encoder_a, encoder_b, encoder_c], mode='concat', concat_axis=-1) drop = Dropout(0.5)(input) flatten = Dense(216)(drop) flatten = Dropout(0.5)(flatten) fc1 = Dense(36, activation='softmax')(flatten) fc2 = Dense(36, activation='softmax')(flatten) fc3 = Dense(36, activation='softmax')(flatten) fc4 = Dense(36, activation='softmax')(flatten) fc5 = Dense(36, activation='softmax')(flatten) fc6 = Dense(36, activation='softmax')(flatten) merged = merge([fc1, fc2, fc3, fc4, fc5, fc6], mode='concat', concat_axis=-1) model = Model(input=input_img, output=merged) else: model = model_from_json(open('model/ba_cnn_model3.json').read()) model.load_weights('model/ba_cnn_model3.h5') # 編譯 # model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) model.summary() model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) # plot(model, to_file='model3.png', show_shapes=True) # 訓練 early_stopping = EarlyStopping(monitor='val_loss', patience=5) model.fit_generator(datagen.flow(x_train, y_train, batch_size=32), samples_per_epoch=len(x_train), nb_epoch=50, validation_data=(x_test, y_test), callbacks=[early_stopping] ) json_string = model.to_json() with open('./model/ba_cnn_model4.json', 'w') as fw: fw.write(json_string) model.save_weights('./model/ba_cnn_model4.h5') print 'done saved model cnn3' # 測試 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))
apicode.py 模型使用
#!/usr/bin/env python # coding=utf-8 from util import CharacterTable from keras.models import model_from_json from PIL import Image import matplotlib.pyplot as plt import os import numpy as np from prepare import clearNoise def img2vec(fname): data = [] img = clearNoise(fname).convert('L') imgM = 1.0 * (np.array(img)>180) print imgM.max(), imgM.min() data.append(imgM.reshape((60, 200, 1))) return np.array(data), imgM ctable = CharacterTable() model = model_from_json(open('model/ba_cnn_model4.json').read()) model.load_weights('model/ba_cnn_model4.h5') def test(path): fnames = [ os.path.join(path, fname) for fname in os.listdir(path) ][:50] correct = 0 for idx, fname in enumerate(fnames, 1): data, imgM = img2vec(fname) y_pred = model.predict(data) result = ctable.decode(y_pred[0]) label = fname.split('/')[-1].split('_')[0] if result == label: correct += 1 print 'correct', fname else: print result, label print 'accuracy: ',idx, float(correct)/idx print '=='*20 # plt.subplot(121) # plt.imshow(Image.open(fname).convert('L'), plt.cm.gray) # plt.title(fname) # # plt.subplot(122) # plt.imshow(imgM, plt.cm.gray) # plt.title(result) # plt.show() test('test')