我加载了我训练的模型。这是培训的最后一层:
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(3))
model.add(Activation('sigmoid'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
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之后,我尝试对随机图像进行预测。所以我加载模型:
#load the model we created
json_file = open('/path/to/model_3.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weight into model
loaded_model.load_weights("/path/to/model_3.h5")
print("\nModel successfully loaded from disk! ")
# Predicting images
img =image.load_img('/path/to/image.jpeg', target_size=(224, 224))
x = image.img_to_array(img)
x *= (255.0/x.max())
image = np.expand_dims(x, axis = 0)
image = preprocess(image)
preds = loaded_model.predict_proba(image)
pred_classes = np.argmax(preds)
print(preds)
print(pred_classes)
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作为输出,我得到这个:
[[6.0599333e-26 0.0000000e+00 1.0000000e+00]]
2
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基本上就像我得到的[0 0 1]
那样predict_classes …
我想添加类似“数字是:8”的内容
<v-slider
class='slider'
step="1"
thumb-label="always"
ticks
tick-size="2"
:max="8"
track-color="#E1E4E9"
thumb-color="#ED5565"
color='#ED5565'
></v-slider>
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