Python ';TypeError:图像数据的无效形状(10000、28、28)';在张量流模式下MNIST图像预测
我在关注TensorFlow上的时尚MNIST图像预测教程;构建并训练模型,但在编写用于绘制预测的函数并尝试绘制预测图像后,会抛出错误: TypeError:图像数据的形状(10000、28、28)无效 整个代码:Python ';TypeError:图像数据的无效形状(10000、28、28)';在张量流模式下MNIST图像预测,python,python-3.x,tensorflow,machine-learning,neural-network,Python,Python 3.x,Tensorflow,Machine Learning,Neural Network,我在关注TensorFlow上的时尚MNIST图像预测教程;构建并训练模型,但在编写用于绘制预测的函数并尝试绘制预测图像后,会抛出错误: TypeError:图像数据的形状(10000、28、28)无效 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.fashio
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)
以下是正确的绘图功能:
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: