8 user defined evaluation function

The user-defined evaluation function is similar to the user-defined loss function , This article defines an evaluation function , Return the correct number

Custom training

from __future__ import absolute_import, division, print_function, unicode_literals

import tensorflow as tf

from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model
import numpy as np

#  On demand ,OOM
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)


#  Data processing 
mnist = np.load("mnist.npz")
x_train, y_train, x_test, y_test = mnist['x_train'],mnist['y_train'],mnist['x_test'],mnist['y_test']

x_train, x_test = x_train / 255.0, x_test / 255.0

# Add a channels dimension
x_train = x_train[..., tf.newaxis]
x_test = x_test[..., tf.newaxis]

train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(32)
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)


#  Build a model 
class MyModel(Model):
    def __init__(self):
        super(MyModel, self).__init__()
        self.conv1 = Conv2D(32, 3, activation='relu')
        self.flatten = Flatten()
        self.d1 = Dense(128, activation='relu')
        self.d2 = Dense(10, activation='softmax')

    def call(self, x):
        x = self.conv1(x)
        x = self.flatten(x)
        x = self.d1(x)
        return self.d2(x)


#  Custom evaluation functions 
#  The returned number is a correct number 
class CatgoricalTruePositives(tf.keras.metrics.Metric):
    def __init__(self, name='categorical_true_positives', **kwargs):
        super(CatgoricalTruePositives, self).__init__(name=name, **kwargs)
        self.true_positives = self.add_weight(name='tp', initializer='zeros')
    
    def update_state(self, y_true, y_pred, sample_weight=None):
        y_pred = tf.argmax(y_pred,axis=-1)
        values = tf.equal(tf.cast(y_true, 'int32'), tf.cast(y_pred, 'int32'))
        values = tf.cast(values, 'float32')
        if sample_weight is not None:
            sample_weight = tf.cast(sample_weight, 'float32')
            values = tf.multiply(values, sample_weight)
        self.true_positives.assign_add(tf.reduce_sum(values))

    def result(self):
        return self.true_positives

    def reset_states(self):
        self.true_positives.assign(0.)


model = MyModel()

loss_object = tf.keras.losses.SparseCategoricalCrossentropy() # Loss function 


optimizer = tf.keras.optimizers.Adam() # Optimizer 

# Evaluation function 
train_loss = tf.keras.metrics.Mean(name='train_loss') #loss

train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy') # Accuracy rate 

train_tp = CatgoricalTruePositives(name="train_tp") # Return the correct number 

test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')
test_tp = CatgoricalTruePositives(name='test_tp')

@tf.function
def train_step(images, labels):
    with tf.GradientTape() as tape:
        predictions = model(images)
        loss = loss_object(labels, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))

    # Evaluate the result of the function 
    train_loss(loss)
    train_accuracy(labels, predictions)
    train_tp(labels, predictions)

@tf.function
def test_step(images, labels):
    predictions = model(images)
    t_loss = loss_object(labels, predictions)

    test_loss(t_loss)
    test_accuracy(labels, predictions)
    test_tp(labels, predictions)


EPOCHS = 5
for epoch in range(EPOCHS):
    #  The next epoch At the beginning of the , Reset evaluation indicator 
    train_loss.reset_states()
    train_accuracy.reset_states()
    train_tp.reset_states()
    test_loss.reset_states()
    test_accuracy.reset_states()
    test_tp.reset_states()


    for images, labels in train_ds:
        train_step(images, labels)

    for test_images, test_labels in test_ds:
        test_step(test_images, test_labels)

    template = 'Epoch {}, Loss: {}, Accuracy: {}, TP: {},Test Loss: {}, Test Accuracy: {}, Test TP:{}'
    print(template.format(epoch + 1,
                          train_loss.result(),
                          train_accuracy.result() * 100,
                          train_tp.result(),
                          test_loss.result(),
                          test_accuracy.result() * 100,
                          test_tp.result()))

fit() Training

from __future__ import absolute_import, division, print_function, unicode_literals

import tensorflow as tf

from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model
import numpy as np

#  On demand ,OOM
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)

mnist = np.load("mnist.npz")
x_train, y_train, x_test, y_test = mnist['x_train'],mnist['y_train'],mnist['x_test'],mnist['y_test']

x_train, x_test = x_train / 255.0, x_test / 255.0

x_train = x_train[..., tf.newaxis]
x_test = x_test[..., tf.newaxis]

y_train = tf.one_hot(y_train,depth=10)
y_test = tf.one_hot(y_test,depth=10)
train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(32)
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).shuffle(100).batch(32)

class MyModel(Model):
    def __init__(self):
        super(MyModel, self).__init__()
        self.conv1 = Conv2D(32, 3, activation='relu')
        self.flatten = Flatten()
        self.d1 = Dense(128, activation='relu')
        self.d2 = Dense(10, activation='softmax')

    def call(self, x):
        x = self.conv1(x)
        x = self.flatten(x)
        x = self.d1(x)
        return self.d2(x)

#  Customize 
# The returned number is a correct number 
#y_true
#y_pred
class CatgoricalTruePositives(tf.keras.metrics.Metric):
    def __init__(self, name='categorical_true_positives', **kwargs):
        super(CatgoricalTruePositives, self).__init__(name=name, **kwargs)
        self.true_positives = self.add_weight(name='tp', initializer='zeros')

    def update_state(self, y_true, y_pred, sample_weight=None):
        y_pred = tf.argmax(y_pred,axis=-1)
        y_true = tf.argmax(y_true,axis=-1)
        values = tf.equal(tf.cast(y_true, 'int32'), tf.cast(y_pred, 'int32'))
        values = tf.cast(values, 'float32')
        if sample_weight is not None:
            sample_weight = tf.cast(sample_weight, 'float32')
            values = tf.multiply(values, sample_weight)
        self.true_positives.assign_add(tf.reduce_sum(values))

    def result(self):
        return self.true_positives

    def reset_states(self):
        self.true_positives.assign(0.)

model = MyModel()
model.compile(optimizer = tf.keras.optimizers.Adam(0.001), # Optimizer 
              loss =  tf.keras.losses.CategoricalCrossentropy(), # Loss function 
              metrics = [tf.keras.metrics.CategoricalAccuracy(),
                         CatgoricalTruePositives(),
                        ]
             ) # Evaluation function 

model.fit(train_ds, epochs=5,validation_data=test_ds)