时间戳

首页 > 解决方案 > tf.keras model.predict 每次提供不同的值

tensorflow - tf.keras model.predict 每次提供不同的值

问题描述

每次我跑：

y_true = np.argmax(tf.concat([y for x, y in train_ds], axis=0), axis=1)
y_pred = np.argmax(model.predict(train_ds), axis=1)

confusion_matrix(y_true, y_pred)

每次结果都与我的理解不同： y_pred = np.argmax(model.predict(train_ds), axis=1)每次都不同。

澄清：我运行单元 1（训练）一次。和单元格 2（推理）几次。

为什么？

代码： 单元格 1（jupyter）

import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, experimental
from tensorflow.keras.layers import MaxPool2D, Flatten, Dense 
from tensorflow.keras import Model
from tensorflow.keras.losses import categorical_crossentropy
from sklearn.metrics import accuracy_score


image_size = (100, 100)
batch_size = 32

train_ds = tf.keras.preprocessing.image_dataset_from_directory(
    directory,
    label_mode='categorical',
    validation_split=0.2,
    subset="training",
    seed=1337,
    color_mode="grayscale",
    image_size=image_size,
    batch_size=batch_size,
)


inputs = Input(shape =(100,100,1))
x = experimental.preprocessing.Rescaling(1./255)(inputs)
x = Conv2D (filters =4, kernel_size =3, padding ='same', activation='relu')(x)
x = Conv2D (filters =4, kernel_size =3, padding ='same', activation='relu')(x)
x = MaxPool2D(pool_size =2, strides =2, padding ='same')(x)

x = Conv2D (filters =8, kernel_size =3, padding ='same', activation='relu')(x)
x = Conv2D (filters =8, kernel_size =3, padding ='same', activation='relu')(x)
x = MaxPool2D(pool_size =2, strides =2, padding ='same')(x)

x = Flatten()(x)
x = Dense(units = 4, activation ='relu')(x)
x = Dense(units = 4, activation ='relu')(x)
output = Dense(units = 5, activation ='softmax')(x)

model = Model (inputs=inputs, outputs =output)

model.compile(
    optimizer=tf.keras.optimizers.Adam(1e-3),
    loss=categorical_crossentropy,
    metrics=["accuracy"])

model.fit(train_ds, epochs=5)

单元格 2：

print (Accuracy:)
y_pred = np.argmax(model.predict(train_ds), axis=1)
print (accuracy_score(y_true, y_pred))

y_pred = np.argmax(model.predict(train_ds), axis=1)
print (accuracy_score(y_true, y_pred))

输出：

118/118 [==============================] - 7s 57ms/步 - 损失：0.1888 - 准确度：0.9398

准确性：

0.593

0.586

标签： tensorflowkerastf.keras

解决方案

您确定每次运行代码时都不会再次训练模型吗？如果模型的参数相同，则相同输入的预测结果每次都应该相同。

推荐阅读