How to optimize a CNN in Keras using precision (instead of accuracy)

user_16813037 0
Viewed 86

My question is simple, I have a target column with True and False values. Basically, it is a binary classification problem. I would like to know how can I optimize my CNN using Precision as a metric instead of Accuracy?

Btw, this's doesn't work:

model.compile(loss='binary_crossentropy',  optimizer=optm, metrics=['precision'])

This is my code:

model = Sequential()
model.add(Dense(64,name = 'Primera', input_dim=8, activation='relu'))
model.add(Dense(32 ,name = 'Segunda'))
model.add(Dense(1,name = 'Tercera', activation='sigmoid'))

from tensorflow.keras import optimizers
optm = optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False)
model.compile(loss='binary_crossentropy',  optimizer=optm, metrics=['accuracy'])

model.summary()

history = model.fit(trainX, trainY, 
                    epochs=1000, 
                    batch_size=16, 
                    validation_split=0.1, 
                    verbose=1)
keras tensorflow classification neural-network python
1 Answers

You can use tf.keras.metrics.Precision(), see the code below for an example.

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.metrics import Precision
from sklearn.datasets import make_classification

X, y = make_classification(n_classes=2, n_features=8, n_informative=8, n_redundant=0, random_state=42)

model = Sequential()
model.add(Dense(64, input_dim=8, activation='relu'))
model.add(Dense(32))
model.add(Dense(1, activation='sigmoid'))

model.compile(
    loss='binary_crossentropy',
    optimizer=Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False),
    metrics=[Precision()]
)

model.fit(X, y, epochs=5, batch_size=32, validation_split=0.1, verbose=1)
# Epoch 1/5
# 3/3 [==============================] - 1s 83ms/step - loss: 0.8535 - precision: 0.5116 - val_loss: 0.6936 - val_precision: 0.5714
# Epoch 2/5
# 3/3 [==============================] - 0s 7ms/step - loss: 0.6851 - precision: 0.5200 - val_loss: 0.5975 - val_precision: 0.6667
# Epoch 3/5
# 3/3 [==============================] - 0s 7ms/step - loss: 0.6004 - precision: 0.6545 - val_loss: 0.5370 - val_precision: 0.8000
# Epoch 4/5
# 3/3 [==============================] - 0s 7ms/step - loss: 0.5412 - precision: 0.8250 - val_loss: 0.4878 - val_precision: 0.8000
# Epoch 5/5
# 3/3 [==============================] - 0s 8ms/step - loss: 0.5145 - precision: 0.9394 - val_loss: 0.4462 - val_precision: 0.8000
user_119890810
Related