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TinyMind人民币面值-热身赛

一、数据加载

# 加载标签import numpy as npimport pandas as pdlabel_path = "/home/jovyan/workspace/train_face_value_label -converted.csv"df = pd.read_csv(label_path)df_label = df.iloc[:,1]label = np.array(df_label)

# 查看标签数据前10个数据print(label[:10])

[8 7 0 5 8 6 3 0 0 1]

# 加载对应类别的金额 import pandas as pdlabelnames_file = "/home/jovyan/workspace/labelnames.csv"labelnames = pd.read_csv(labelnames_file)print(labelnames)

classid  labelname0        0        0.11        1        0.22        2        0.53        3        1.04        4        2.05        5        5.06        6       10.07        7       50.08        8      100.0

# 定义类别classid转换成labelname的函数label_names = np.array(labelnames)def id_to_name(id):    return label_names[id][1]

# 加载训练集图片数据# 读取图片数据import numpy as npimport glob # 查找符合特定规则的文件路径名from PIL import Imagetrain_image_path = "/home/jovyan/workspace/train_data/*.jpg"train_data = np.zeros((39620,150,300,1))i = 0for imageFile in glob.glob(train_image_path):    img_arr = np.array(Image.open(imageFile).convert("L")).reshape(150,300,1)    train_data[i] = img_arr    i += 1

# 划分训练集和验证集x_train = train_data[:30000]y_train = label[:30000]x_valid = train_data[30000:]y_valid = label[30000:]

二、探索数据集和标签

# 查看训练集的数量print("the number of training examples:",x_train.shape[0])# 查看测试集的数量print("the number of valid examples:",x_valid.shape[0])# 查看训练标签的数量print("the number of training label:",len(y_train))# 查看测试标签的数量print("the number of valid label:",len(y_valid))# 查看数据格式print("the image data shape=",x_train.shape[1:])

the number of training examples: 30000the number of valid examples: 9620the number of training label: 30000the number of valid label: 9620the image data shape= (150, 300, 1)

# 查看数据标签的数量import numpy as nplabel_name = np.unique(y_train)label_sum = len(label_name)print("the labels is:",label_name)print("the length of label is:",label_sum)

the labels is: [0 1 2 3 4 5 6 7 8]the length of label is: 9

# 直方图来展示图像训练集的各个类别的分布情况import matplotlib.pyplot as plt%matplotlib inlinen_classes = len(np.unique(y_train))def plot_y_train_hist():    fig = plt.figure(figsize=(15,5))    ax = fig.add_subplot(1,1,1)    hist = ax.hist(y_train,bins = n_classes)    ax.set_title("the frequency of each category sign")    ax.set_xlabel("signs")    ax.set_ylabel("frequency")    plt.show()    return histprint(x_train.shape)print(y_train.shape)hist = plot_y_train_hist()

(30000, 150, 300, 1)(30000,)

# 绘制money图import matplotlib.pyplot as plt%matplotlib inlinefig,axes = plt.subplots(2,5,figsize=(18,5))ax_array = axes.ravel()for ax in ax_array:    index = np.random.randint(0,len(x_train))    ax.imshow(x_train[index].reshape(150,300))    ax.axis("off")    ax.set_title(id_to_name(y_train[index]))plt.show()

三、数据集中处理

# 图像数据归一化处理x_train = np.array(x_train,dtype=np.float32)x_valid = np.array(x_valid,dtype=np.float32)X_train = (x_train-128)/128X_valid = (x_valid-128)/128

print(X_train[0])

[[[ 0.8203125]  [ 0.8203125]  [ 0.8125   ]  ...,   [ 0.78125  ]  [ 0.7890625]  [ 0.8046875]] [[ 0.8203125]  [ 0.8203125]  [ 0.8203125]  ...,   [ 0.78125  ]  [ 0.7890625]  [ 0.8046875]] [[ 0.8203125]  [ 0.8203125]  [ 0.8203125]  ...,   [ 0.78125  ]  [ 0.7890625]  [ 0.8046875]] ...,  [[ 0.9609375]  [ 0.9609375]  [ 0.96875  ]  ...,   [ 0.9296875]  [ 0.9296875]  [ 0.9296875]] [[ 0.9609375]  [ 0.9609375]  [ 0.96875  ]  ...,   [ 0.9375   ]  [ 0.9375   ]  [ 0.9375   ]] [[ 0.9609375]  [ 0.9609375]  [ 0.96875  ]  ...,   [ 0.9375   ]  [ 0.9453125]  [ 0.9453125]]]

print(X_train.shape,type(X_train))print(X_valid.shape,type(X_valid))

(30000, 150, 300, 1) 
   
    (9620, 150, 300, 1) 
    
   

# 标签数据one-hot编码处理from tensorflow import kerasfrom keras.utils import np_utilsprint("Shape before one-hot encoding:",y_train.shape)Y_train = np_utils.to_categorical(y_train,label_sum)Y_valid = np_utils.to_categorical(y_valid,label_sum)print("Shape after one-hot encoding:",Y_train.shape)print("Shape after one-hot encoding:",Y_valid.shape)

Shape before one-hot encoding: (30000,)Shape after one-hot encoding: (30000, 9)Shape after one-hot encoding: (9620, 9)Using TensorFlow backend.

四、模型的建立

from keras.models import Sequentialfrom keras.layers import Dense,Dropout,Flattenfrom keras.layers import Conv2D,MaxPooling2Dmodel = Sequential()# layers1model.add(Conv2D(filters=32,                 kernel_size=(3,3),                 input_shape=X_train.shape[1:],                 activation="relu"))model.add(MaxPooling2D(pool_size=(2,2)))model.add(Dropout(0.5))# layers2model.add(Conv2D(filters=32,                 kernel_size=(3,3),                 activation="relu"))model.add(MaxPooling2D(pool_size=(2,2)))model.add(Dropout(0.5))# layers3model.add(Conv2D(filters=32,                 kernel_size=(3,3),                 activation="relu"))model.add(MaxPooling2D(pool_size=(2,2)))model.add(Dropout(0.5))# flatternmodel.add(Flatten())# Densemodel.add(Dense(label_sum,activation="softmax"))

# 查看模型结构model.summary()

_________________________________________________________________Layer (type)                 Output Shape              Param #   =================================================================conv2d_1 (Conv2D)            (None, 148, 298, 32)      320       _________________________________________________________________max_pooling2d_1 (MaxPooling2 (None, 74, 149, 32)       0         _________________________________________________________________dropout_1 (Dropout)          (None, 74, 149, 32)       0         _________________________________________________________________conv2d_2 (Conv2D)            (None, 72, 147, 32)       9248      _________________________________________________________________max_pooling2d_2 (MaxPooling2 (None, 36, 73, 32)        0         _________________________________________________________________dropout_2 (Dropout)          (None, 36, 73, 32)        0         _________________________________________________________________conv2d_3 (Conv2D)            (None, 34, 71, 32)        9248      _________________________________________________________________max_pooling2d_3 (MaxPooling2 (None, 17, 35, 32)        0         _________________________________________________________________dropout_3 (Dropout)          (None, 17, 35, 32)        0         _________________________________________________________________flatten_1 (Flatten)          (None, 19040)             0         _________________________________________________________________dense_1 (Dense)              (None, 9)                 171369    =================================================================Total params: 190,185Trainable params: 190,185Non-trainable params: 0_________________________________________________________________

# 编译模型model.compile(loss="categorical_crossentropy",              metrics=["accuracy"],              optimizer="adam")

# 训练模型history = model.fit(X_train,                    Y_train,                    batch_size=50,                    epochs=5,                    verbose=2,                    validation_data=(X_valid,Y_valid))

Train on 30000 samples, validate on 9620 samplesEpoch 1/5 - 65s - loss: 0.0561 - acc: 0.9846 - val_loss: 0.0131 - val_acc: 0.9988Epoch 2/5 - 61s - loss: 0.0146 - acc: 0.9985 - val_loss: 0.0109 - val_acc: 0.9993Epoch 3/5 - 61s - loss: 0.0105 - acc: 0.9993 - val_loss: 0.1261 - val_acc: 0.9598Epoch 4/5 - 60s - loss: 0.0191 - acc: 0.9981 - val_loss: 0.2859 - val_acc: 0.9885Epoch 5/5 - 60s - loss: 0.0120 - acc: 0.9989 - val_loss: 0.0092 - val_acc: 0.9992

# 保存模型import osimport tensorflow.gfile as gfilesave_dir = "/home/jovyan/workspace/mondel/"if gfile.Exists(save_dir):    gfile.DeleteRecursively(save_dir)gfile.MakeDirs(save_dir)model_name = 'keras_money_v1.h5'model_path = os.path.join(save_dir, model_name)model.save(model_path)print('Saved trained model at %s ' % model_path)

Saved trained model at /home/jovyan/workspace/mondel/keras_money_v1.h5

五、预测数据

1.待预测数据导入并保存图片名称

# 保存图片名称import osimport numpy as nptest_image_path = "/home/jovyan/workspace/test_data/"write_file_name = "/home/jovyan/workspace/name.txt"test_image_list = []for image_name in os.listdir(test_image_path):    test_image_list.append(image_name)

number_of_lines = len(test_image_list)print(number_of_lines)

20000

# 写入txt文件中，逐行写入write_file = open(write_file_name,"w")for current_line in range(number_of_lines):    write_file.write(test_image_list[current_line]+"\n")# 关闭文件write_file.close()

********* 分割线 *********

# 加载预测数据import numpy as npimport globfrom PIL import Imagetest_image_path = "/home/jovyan/workspace/test_data/*.jpg"test_data = np.zeros((20000,150,300,1))i = 0for imageFile in glob.glob(test_image_path):    img_arr = np.array(Image.open(imageFile).convert("L")).reshape(150,300,1)       test_data[i] = img_arr    i += 1

# 查看测试集的数量print("the number of training examples:",test_data.shape[0])# 查看数据格式print("the image data shape=",test_data.shape[1:])

the number of training examples: 20000the image data shape= (150, 300, 1)

2.带预测数据预处理

x_test = np.array(test_data,dtype=np.float32)X_test = (x_test-128)/128

print(X_test.shape,type(X_test))

(20000, 150, 300, 1) 
   

3.模型对待预测数据预测

# 对带预测数据进行一寸res = model.predict_classes(X_test)

# 查看预测结果前10个print(res[:10])

[2 4 6 1 3 3 1 2 6 1]

# 查看预测结果的类型print(res.shape,type(res))

(20000,) 
   

# id转金额res_converted = np.zeros((20000))for i in range(len(res)):    res_converted[i] = id_to_name(res[i])

# 查看预测结果转换金额后的前10个print(res_converted[:10])

[ 0.5  2.  10.   0.2  1.   1.   0.2  0.5 10.   0.2]

print(res_converted.shape,type(res_converted))

(20000,) 
   

# 将预测值保存至txt文档中import numpy as npnp.savetxt("res.txt",res_converted,fmt='%.1f')

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