如何使用經過訓練的 CNN 對新圖像進行分類？

Question

我按照教程在 MNIST 數據集上創建了一個帶有 Keras 的 CNN。 我想在我下載的全新圖像上測試我的 model。 我的理解是我必須將圖像轉換為黑白，然后將其轉換為 numpy 數組。 從那里我應該能夠將 numpy 數組輸入到 model.predict 但我得到一個錯誤。 有沒有一種簡單的方法可以通過簡單地將我的新圖像的路徑提供給 model 來在新圖像上測試我的 model？ 下面是我試過的代碼

from PIL import Image 
from numpy import asarray
img = Image.open("Eight.png") # open colour image
img = img.convert('1') # convert image to black and white
img.save('eight_BW.png')

# load the image and convert into 
# numpy array 
img = Image.open('eight_bw.png') 
  
# asarray() class is used to convert 
# PIL images into NumPy arrays 
numpydata = asarray(img, dtype=int) 
  
# <class 'numpy.ndarray'> 
print(type(numpydata)) 
  
#  shape 
print(numpydata.shape) 

model.predict(numpydata)

<class 'numpy.ndarray'> (55, 72)

ValueError: Input 0 of layer sequential_2 is incompatible with the layer: : expected min_ndim=4, found ndim=2. Full shape received: [None, 72]

下面是 model 代碼：

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

print(x_train.shape, y_train.shape)

x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
input_shape = (28, 28, 1)

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train)
y_test = keras.utils.to_categorical(y_test)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

batch_size = 128
num_classes = 10
epochs = 10

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),activation='relu',input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,optimizer=keras.optimizers.Adadelta(),metrics=['accuracy'])

hist = model.fit(x_train, y_train,batch_size=batch_size,epochs=epochs,verbose=1,validation_data=(x_test, y_test))
print("The model has successfully trained")

model.save('mnist.h5')
print("Saving the model as mnist.h5")

Answer 1

在numpydata = asarray(img, dtype=int)和Tada 之后添加這兩行！

numpydata = np.expand_dims(numpydata, 0)
numpydata = np.expand_dims(numpydata, -1)

為什么：首先 model 接受一批圖像。 因此，您可以同時傳遞例如 32 張圖像。 但是，如果您只想通過 model 一張圖像，您的批次只有一張圖像，模型輸入形狀應為 (1, ..image_shape..) （開頭的額外維度已由numpydata = np.expand_dims(numpydata, 0) ）。 另一方面，即使您的圖像有一個通道（通道 == 1），您的圖像也應該具有三個維度（高度、寬度、通道），因此我們在末尾添加了額外的維度numpydata = np.expand_dims(numpydata, -1)