昔作ったCNNの簡単な(2層)モデルが出てきたので、貼っておく

昔作ったCNNの簡単な(2層)モデルが出てきたので、貼っておく:

はじめに

昔、画像認識での正誤判定にkerasでモデル作成を行ったことがあるが、

その際のソースコードのかけらを発見したので、Qiita上にサルベージしておく。

CNN

2_layer_cnn.py

from keras.models import Sequential 
from keras.layers import Activation, Dense, Dropout 
from keras.utils.np_utils import to_categorical 
from keras.optimizers import Adagrad 
from keras.optimizers import Adam 
import numpy as np 
from PIL import Image 
import os 
 
# 学習用のデータを作る. 
image_list = [] 
label_list = [] 
 
for dir in os.listdir("data/dog/ForFinalTrain/"): 
    dir1 = "data/dog/ForFinalTrain/" + dir 
    label = 0 
 
    if dir == "Normal": 
        label = 0 
    elif dir == "Tumor": 
        label = 1 
 
    for file in os.listdir(dir1): 
        if file != "Thumbs.db": 
            label_list.append(label) 
            filepath = dir1 + "/" + file 
            image = np.array(Image.open(filepath).resize((25,25))) 
            image = image.transpose(2,0,1) 
            image = image.reshape(1, image.shape[0] * image.shape[1] * image.shape[2]).astype("float32")[0] 
            image_list.append(image / 255.) 
 
image_list = np.array(image_list) 
Y = to_categorical(label_list) 
 
''' 
model = Sequential() 
model.add(Dense(200, input_dim=1875)) 
model.add(Activation("relu")) 
model.add(Dropout(0.2)) 
 
model(Dense(200)) 
model.add(Activation("relu")) 
model.add(Dropout(0.2)) 
 
model.add(Dense(2)) 
model.add(Activation("softmax")) 
 
opt = Adam(Ir=0.001) 
 
model.compile(loss="categorical_crossentropy",optimizer=opt, metrics=["accuracy"]) 
''' 
model = Sequential() 
model.add(Dense(32, activation='relu', input_dim=1875)) 
model.add(Dense(2, activation='sigmoid')) 
model.compile(optimizer='rmsprop', 
              loss='binary_crossentropy', 
              metrics=['accuracy']) 
 
model.fit(image_list, Y,nb_epoch=1500, batch_size=100, validation_split=0.1) 
 
total = 0. 
ok_count = 0. 
 
for dir in os.listdir("data/dog/ForFinalTrain/"): 
    if dir == "Thumbs.db": 
        continue 
 
    dir1 = "data/dog/ForFinalTest/" + dir 
    label = 0 
 
    if dir == "Normal": 
        label = 0 
    elif dir == "Abnormal": 
        label = 1 
 
    for file in os.listdir(dir1): 
        if file != "Thumbs.db": 
            label_list.append(label) 
            filepath = dir1 + "/" + file 
            image = np.array(Image.open(filepath).resize((25, 25))) 
            print(filepath) 
            image = image.transpose(2, 0, 1) 
            image = image.reshape(1, image.shape[0] * image.shape[1] * image.shape[2]).astype("float32")[0] 
            result = model.predict_classes(np.array([image / 255.])) 
            print("label:", label, "result:", result[0]) 
 
            total += 1. 
 
            if label == result[0]: 
                ok_count += 1. 
 
print("seikai: ", ok_count / total * 100, "%")