[英]How to get precision and recall, for a keras model?
i want to see precision and recall for my model for a binary image classification but i can find how do to that我想查看我的 model 的精度和召回率,以进行二进制图像分类,但我可以找到如何做到这一点
Here is my code这是我的代码
x = base_model.output
x = tf.keras.layers.GlobalAveragePooling2D()(x)
x = tf.keras.layers.Dense(1024, activation='relu')(x)
x = tf.keras.layers.Dense(1024, activation='relu')(x)
x = tf.keras.layers.Dense(512, activation='relu')(x)
preds = tf.keras.layers.Dense(2, activation='softmax')(x)
model = tf.keras.Model(inputs = base_model.input, outputs = preds)
for layer in model.layers[:175]:
layer.trainable = False
for layer in model.layers[175:]:
layer.trainable = True
model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit_generator(generator=train_generator,
epochs=20,
steps_per_epoch=step_size_train,
validation_data = test_generator,
validation_steps=step_size_test)```
If you want precision and recall during train then you can add precision and recall metrics to the metrics
list during model compilation as below如果您想要训练期间的精确度和召回率,那么您可以在 model 编译期间将精确度和召回率指标添加到
metrics
列表中,如下所示
model.compile(optimizer='Adam', loss='categorical_crossentropy',
metrics=['accuracy',
tf.keras.metrics.Precision(),
tf.keras.metrics.Recall()])
input = tf.keras.layers.Input(8)
x = tf.keras.layers.Dense(4, activation='relu')(input)
output = tf.keras.layers.Dense(2, activation='softmax')(x)
model = tf.keras.Model(inputs = input, outputs = output)
model.compile(optimizer='Adam', loss='categorical_crossentropy',
metrics=['accuracy',
tf.keras.metrics.Precision(),
tf.keras.metrics.Recall()])
X = np.random.randn(100,8)
y = np.random.randint(0,2, (100, 2))
model.fit(X, y, epochs=10)
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