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python - 为什么 Keras 训练良好但返回错误的预测？

问题描述

如果我给模型喂五朵 Setosas 花，我无法让我的模型预测它们确实是 Setosas。

这是我的代码设置：

# Load libraries
import numpy as np
import pandas as pd
from keras import models
from keras import layers
from keras.models import Sequential
from keras.layers import Dense
from sklearn.utils import shuffle
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV

# Set random seed
np.random.seed(0)

# Step 1: Load data
iris = pd.read_csv("iris.csv")

X = iris.drop('species', axis=1)
y = pd.get_dummies(iris['species']).values

# Step 2: Preprocess data
scaler = preprocessing.StandardScaler() 
X = scaler.fit_transform(X)

X, y = shuffle(X, y)

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

network = models.Sequential()
network.add(layers.Dense(units=8, activation="relu", input_shape=(4,)))
network.add(layers.Dense(units=3, activation="softmax"))

# Compile neural network
network.compile(loss="categorical_crossentropy", 
                optimizer="adam", 
                metrics=["accuracy"]) 

# Train neural network
history = network.fit(X_train, # Features
                      y_train, # Target
                      epochs= 200, 
                      verbose= 1, 
                      batch_size=10, # Number of observations per batch
                      validation_data=(X_test, y_test)) # Test data

模型训练得很好，这是最后一个 epoch：

Epoch 200/200
112/112 [==============================] - 0s 910us/step - loss: 0.0740 - acc: 0.9911 - val_loss: 0.1172 - val_acc: 0.9737

现在，让我们进行一些预测。

new_iris = iris.iloc[0:5, 0:4] # pull out the first five Setosas from original iris dataset; 
# prediction should give me Setosa since I am feeding it Setosas

np.around(network.predict(new_iris), decimals = 2) # predicts versicolor with high probability

array([[0.  , 0.95, 0.04],
       [0.  , 0.94, 0.06],
       [0.  , 0.96, 0.04],
       [0.  , 0.91, 0.09],
       [0.  , 0.96, 0.04]], dtype=float32)\

关于为什么会这样的任何想法？

标签： pythonkeras

解决方案

您需要在测试时应用在训练期间学到的转换。

new_iris = iris.iloc[0:5, 0:4] # pull out the first five Setosas from original iris dataset; 
new_iris = scaler.transform(new_iris)
np.around(network.predict(new_iris), decimals = 2)

输出

array([[1.  , 0.  , 0.  ],
       [0.99, 0.01, 0.  ],
       [1.  , 0.  , 0.  ],
       [0.99, 0.01, 0.  ],
       [1.  , 0.  , 0.  ]], dtype=float32)

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