thaanaocr.py 文件源码

def on_epoch_end(self, epoch, logs={}):

        font = {'family': 'Thaana Unicode Akeh',
                'color':  'darkred',
                'weight': 'normal',
                'size': 12,
        }

        self.model.save_weights(os.path.join(self.output_dir, 'weights%02d.h5' % (epoch)))

        #self.model.save('model.h5')  # creates a HDF5 file 

        self.show_edit_distance(256)
        self.num_display_words = 6 
        word_batch = next(self.text_img_gen)[0]
        # add visual augmentations 
        res = decode_batch(self.test_func, word_batch['the_input'][0:6])
        if word_batch['the_input'][0].shape[0] < 256:
            cols = 2
        else:
            cols = 1
        for i in range(self.num_display_words):
            plt.subplot(self.num_display_words // cols, cols, i + 1)

            if K.image_data_format() == 'channels_first':
                the_input = word_batch['the_input'][i, 0, :, :]
            else:
                the_input = word_batch['the_input'][i, :, :, 0]
            plt.imshow(the_input.T, cmap='Greys_r')
            plt.xlabel('Actual = \'%s\'\Prediction = \'%s\'' % (word_batch['source_str'][i][::-1], res[i][::-1]),fontdict=font)
            #print (('Actual = \'%s\'\Prediction = \'%s\'' % (word_batch['source_str'][i], res[i])))
        fig = plt.gcf()

        fig.set_size_inches(10, 13)
        plt.savefig(os.path.join(self.output_dir, 'e%02d.png' % (epoch)))

        plt.close()