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python - 'TypeError: Invalid shape (10000, 28, 28) for image data' in tensor flow Fashion MNIST image prediction

问题描述

我正在关注 TensorFlow 上的 Fashion MNIST 图像预测教程；构建并训练了模型，但在编写了绘制预测的函数并尝试绘制预测图像时，它抛出了错误：

TypeError：图像数据的形状无效（10000、28、28）

整个代码：

import tensorflow as tf
import numpy as np
from tensorflow import keras
import matplotlib.pyplot as plt

fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 
'Sneaker', 'Bag', 'Ankle boot']

plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid = False

train_images = train_images/255
test_images = test_images/255

plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid=False
    plt.imshow(train_images[i], cmap=plt.cm.binary)
    plt.xlabel(class_names[train_labels[i]])

model = keras.Sequential([keras.layers.Flatten(input_shape=(28,28)),keras.layers.Dense(128, 
activation ='relu'),keras.layers.Dense(10)])



model.compile(optimizer='adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics=['accuracy'])

model.fit(train_images, train_labels, epochs=50)

test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print('Test Accuracy:', test_acc)

probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])
predictions = probability_model.predict(test_images)

predictions[0]

np.argmax(predictions[0])

def plot_image(i, predictions_array, true_label, img):
    predictions_array, true_labels, image=predictions_array, true_label[i], img[i]
    plt.grid=False
    plt.xticks([])
    plt.yticks([])

    plt.imshow(img, cmap=plt.cm.binary)
    predicted_label = np.argmax(predictions_array)

    if predicted_label == true_label:
        color = 'blue'
    else:
        color = 'red'
    
    plt.xlabel('{}{:2.0f}%({})'.format(class_names[predicted_label], 100*np.max[predictions_array], class_names[true_label]),color=color)

def plot_value_array(i, predictions_array, true_label):
    predictions_array, true_label = predictions_array, true_label[i]
    plt.grid=False
    plt.xticks(range(10))
    plt.yticks([])
    thisplot = plt.bar(range(10), predictions_array, color='#777777')
    predicted_label = np.argmax(predictions_array)

    thisplot[predicted_label].set_color('red')
    thisplot[true_label].set_color('blue')

i = 0
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions[i], test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions[i],  test_labels)

标签： pythonpython-3.xtensorflowmachine-learningneural-network

解决方案

这里是正确的绘图功能：

def plot_image(i, predictions_array, true_label, img):
    predictions_array, true_labels, img = predictions_array, true_label[i], img[i]
    plt.grid=False
    plt.xticks([])
    plt.yticks([])

    plt.imshow(img, cmap=plt.cm.binary)
    predicted_label = np.argmax(predictions_array)

    if predicted_label == true_labels:
        color = 'blue'
    else:
        color = 'red'
    
    print(class_names[predicted_label], 100*np.max(predictions_array), class_names[true_labels])

    plt.xlabel('{}{:2.0f}%({})'.format(class_names[predicted_label], 100*np.max(predictions_array), class_names[true_labels]), color=color)

工作示例：https ://colab.research.google.com/drive/1owyRzS5lRW6DDc3p7D13D8Ih0yoj6Rz5?usp=sharing

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