[英]Getting accuracy: 0.0000e+00 in my Tensor flow model
I was trying to practice my skills in CNN and deep learning by solving a Challenge on Kaggle.我试图通过解决 Kaggle 上的挑战来练习我在 CNN 和深度学习方面的技能。 It's a Regression-based.
它是基于回归的。 This is the model - MODEL
这是 model - MODEL
the dataset is - https://www.kaggle.com/piantic/osic-pulmonary-fibrosis-progression-basic-eda数据集是 - https://www.kaggle.com/piantic/osic-pulmonary-fibrosis-progression-basic-eda
model = model_architechture()
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.01), loss='mae',metrics= 'accuracy')
tr_p, vl_p = train_test_split(P, shuffle=True, train_size= 0.8)
er = tf.keras.callbacks.EarlyStopping(
monitor="val_loss",
min_delta=1e-3,
patience=5,
verbose=0,
mode="auto",
baseline=None,
restore_best_weights=True,
)
model.fit_generator(IGenerator(keys=tr_p,
a = A,
tab = TAB),
steps_per_epoch = 50,
validation_data=IGenerator(keys=vl_p,
a = A,
tab = TAB),
validation_steps = 10,
callbacks = [er],
epochs=5)
This is the output i am getting.这是我得到的 output。 As you can see, the accuracy is 0. Is there a way i can calculate the accuracy of this model.
如您所见,精度为 0。有没有办法可以计算此 model 的精度。
Epoch 1/5
50/50 [==============================] - 23s 467ms/step - loss: 17.4784 - accuracy: 0.0000e+00 - val_loss: 6.6384 - val_accuracy: 0.0000e+00
Epoch 2/5
50/50 [==============================] - 23s 458ms/step - loss: 5.4008 - accuracy: 0.0000e+00 - val_loss: 3.8762 - val_accuracy: 0.0000e+00
Epoch 3/5
50/50 [==============================] - 23s 453ms/step - loss: 4.7755 - accuracy: 0.0000e+00 - val_loss: 4.3907 - val_accuracy: 0.0000e+00
Epoch 4/5
50/50 [==============================] - 23s 456ms/step - loss: 4.8971 - accuracy: 0.0000e+00 - val_loss: 4.0197 - val_accuracy: 0.0000e+00
Epoch 5/5
50/50 [==============================] - 23s 461ms/step - loss: 4.7031 - accuracy: 0.0000e+00 - val_loss: 3.9652 - val_accuracy: 0.0000e+00
Accuracy metric is used to measure the classification accuracy.准确度度量用于衡量分类准确度。 It can not be used to measure regression.
它不能用于衡量回归。 Not always predictions are equal to expected values, even it differ from small value it leads to zero accuracy.
预测并不总是等于预期值,即使它与小值不同也会导致零准确度。
Use regression metric such as mean absolute error
, mean squared error
, r2 score
.使用回归度量,例如
mean absolute error
、 mean squared error
、 r2 score
。
List of Regression metrics can found here , choose which is appropriate for your problem.可以在此处找到回归指标列表,选择适合您问题的指标。
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