low test accuracy

Hi, I use your ArcFace to train and test FashionMnist dataset. According to your guildline, the test accuracy is less than 0.1. Your test code seems not work very well.

Can you provide a copy of model test code which can achieve good test accuracy? Thanks.

[4uiiurz1]

@Liang-yc

Sry! I misunderstood! Plz try this code:

W = arcface_model.get_layer('output').W
arcface_model = Model(inputs=arcface_model.input[0], outputs=arcface_model.layers[-3].output)
arcface_features = arcface_model.predict(X1i[0], verbose=0)
arcface_features /= np.linalg.norm(arcface_features, axis=1, keepdims=True)
yTrue = np.argmax(X1i[1], axis=1)
yPred = np.argmax(K.eval(arcface_features @ W), axis=1)
accuracy = metrics.accuracy_score(yTrue, yPred) * 100
error = 100 - accuracy
print(yTrue, yPred, np.max(yPred))
print("Accuracy : ", accuracy)
print("Error : ", error)

[almoghitelman]

Hi, I tried to implement the code above in order to get prediction on test image, but the (K.eval(arcface_features @ W)) returns a matrix with negative values which don’t sum to 1. the all training process is build as same as provided here. As I understood the predicted matrix values are prob which sum to 1. is it correct? where is the problem in my code? results and model attached.

Thanks!

Model: “model_1”

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Layer (type) Output Shape Param # Connected to

input_1 (InputLayer) (None, 112, 112, 3) 0

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conv2d_1 (Conv2D) (None, 112, 112, 16) 448 input_1[0][0]

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batch_normalization_1 (BatchNor (None, 112, 112, 16) 64 conv2d_1[0][0]

---

activation_1 (Activation) (None, 112, 112, 16) 0 batch_normalization_1[0][0]

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conv2d_2 (Conv2D) (None, 112, 112, 16) 2320 activation_1[0][0]

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batch_normalization_2 (BatchNor (None, 112, 112, 16) 64 conv2d_2[0][0]

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activation_2 (Activation) (None, 112, 112, 16) 0 batch_normalization_2[0][0]

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max_pooling2d_1 (MaxPooling2D) (None, 56, 56, 16) 0 activation_2[0][0]

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conv2d_3 (Conv2D) (None, 56, 56, 32) 4640 max_pooling2d_1[0][0]

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batch_normalization_3 (BatchNor (None, 56, 56, 32) 128 conv2d_3[0][0]

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activation_3 (Activation) (None, 56, 56, 32) 0 batch_normalization_3[0][0]

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conv2d_4 (Conv2D) (None, 56, 56, 32) 9248 activation_3[0][0]

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batch_normalization_4 (BatchNor (None, 56, 56, 32) 128 conv2d_4[0][0]

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activation_4 (Activation) (None, 56, 56, 32) 0 batch_normalization_4[0][0]

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max_pooling2d_2 (MaxPooling2D) (None, 28, 28, 32) 0 activation_4[0][0]

---

conv2d_5 (Conv2D) (None, 28, 28, 64) 18496 max_pooling2d_2[0][0]

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batch_normalization_5 (BatchNor (None, 28, 28, 64) 256 conv2d_5[0][0]

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activation_5 (Activation) (None, 28, 28, 64) 0 batch_normalization_5[0][0]

---

conv2d_6 (Conv2D) (None, 28, 28, 64) 36928 activation_5[0][0]

---

batch_normalization_6 (BatchNor (None, 28, 28, 64) 256 conv2d_6[0][0]

---

activation_6 (Activation) (None, 28, 28, 64) 0 batch_normalization_6[0][0]

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max_pooling2d_3 (MaxPooling2D) (None, 14, 14, 64) 0 activation_6[0][0]

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conv2d_7 (Conv2D) (None, 14, 14, 128) 73856 max_pooling2d_3[0][0]

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batch_normalization_7 (BatchNor (None, 14, 14, 128) 512 conv2d_7[0][0]

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activation_7 (Activation) (None, 14, 14, 128) 0 batch_normalization_7[0][0]

---

conv2d_8 (Conv2D) (None, 14, 14, 128) 147584 activation_7[0][0]

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batch_normalization_8 (BatchNor (None, 14, 14, 128) 512 conv2d_8[0][0]

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activation_8 (Activation) (None, 14, 14, 128) 0 batch_normalization_8[0][0]

---

max_pooling2d_4 (MaxPooling2D) (None, 7, 7, 128) 0 activation_8[0][0]

---

conv2d_9 (Conv2D) (None, 7, 7, 256) 295168 max_pooling2d_4[0][0]

---

batch_normalization_9 (BatchNor (None, 7, 7, 256) 1024 conv2d_9[0][0]

---

activation_9 (Activation) (None, 7, 7, 256) 0 batch_normalization_9[0][0]

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conv2d_10 (Conv2D) (None, 7, 7, 256) 590080 activation_9[0][0]

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batch_normalization_10 (BatchNo (None, 7, 7, 256) 1024 conv2d_10[0][0]

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activation_10 (Activation) (None, 7, 7, 256) 0 batch_normalization_10[0][0]

---

max_pooling2d_5 (MaxPooling2D) (None, 3, 3, 256) 0 activation_10[0][0]

---

batch_normalization_11 (BatchNo (None, 3, 3, 256) 1024 max_pooling2d_5[0][0]

---

dropout_1 (Dropout) (None, 3, 3, 256) 0 batch_normalization_11[0][0]

---

flatten_1 (Flatten) (None, 2304) 0 dropout_1[0][0]

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dense_1 (Dense) (None, 128) 295040 flatten_1[0][0]

---

batch_normalization_12 (BatchNo (None, 128) 512 dense_1[0][0]

---

input_2 (InputLayer) (None, 47) 0

---

arc_face_1 (ArcFace) (None, 47) 6016 batch_normalization_12[0][0]
input_2[0][0]

x_new = np.expand_dims(x_new, axis=0)
x_new = np.array(x_new, dtype=np.float32) / 255.0

arcface_model = load_model(Params['model_path'], custom_objects={'ArcFace': ArcFace})
arcface_model.summary()
W = arcface_model.get_layer('arc_face_1').W
#W = arcface_model.get_weights
arcface_model = Model(inputs=arcface_model.input[0], outputs=arcface_model.layers[-3].output)
arcface_features = arcface_model.predict(x_new, verbose=0)
arcface_features /= np.linalg.norm(arcface_features, axis=1, keepdims=True)
print(W)
print(arcface_features)
#yTrue = np.argmax(X1i[1], axis=1)
calc = K.eval(arcface_features @ W)
yPred = np.argmax(calc, axis=1)
print(calc)

calc matrix: [[-1.3757099 -1.3127273 -1.0342306 -1.052017 -1.3646903 -0.99030894 -1.0056239 -0.9335255 -1.0159407 -0.9508162 -0.87866336 -1.1595434 -1.0569867 -1.0423982 -1.1982433 -1.1383088 -0.95849425 -1.0996643 -0.96682745 -0.96783435 -1.1292582 -1.1419181 -1.213215 -1.1428651 -1.8159819 -1.8428197 -1.4751792 -2.1246967 -2.9295607 -1.2175179 -1.458914 -5.2540975 -2.6965609 -0.25673357 -1.9802661 -1.4791859 -2.3778896 -2.2576988 -2.724395 -1.3704951 -4.3583426 -0.5972382 -0.281455 -0.49513057 -0.68900996 -3.0312972 -0.28473428]]