Kerasで花画像の名前を当てる~検出編~[Keras][CNN][深層学習][Python]

import numpy as np
from keras.preprocessing.image import load_img, img_to_array
from keras.applications.vgg16 import VGG16, preprocess_input
from keras.models import Sequential, Model
from keras.layers import Input, Dense, Dropout, Activation, Flatten
from keras.optimizers import SGD
classes = ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']
nb_classes = len(classes)
img_rows, img_cols = 224, 224
def build_model() :
input_tensor = Input(shape=(img_rows, img_cols, 3))
vgg16 = VGG16(include_top=False, weights='imagenet', input_tensor=input_tensor)
_model = Sequential()
_model.add(Flatten(input_shape=vgg16.output_shape[1:]))
_model.add(Dense(256, activation='relu'))
_model.add(Dropout(0.5))
_model.add(Dense(nb_classes, activation='softmax'))
model = Model(inputs=vgg16.input, outputs=_model(vgg16.output))
for layer in model.layers[:15]:
layer.trainable = False
model.compile(loss='categorical_crossentropy',
optimizer=SGD(lr=1e-4, momentum=0.9), metrics=['accuracy'])
return model
if __name__ == "__main__":
model = build_model()
# 学習時に使ったモデルを使用
model.load_weights("./flower-model.hdf5")
filename = "./daisy.jpg"
# kerasに画像を読み込む関数がある
img = load_img(filename, target_size=(img_rows, img_cols))
x = img_to_array(img)
x = np.expand_dims(x, axis=0)
predict = model.predict(preprocess_input(x))
for pre in predict:
y = pre.argmax()
print("Name: ", classes)

>> Name: daisy