[英]How do I solve the issue of ValueError with Tensorflow and Keras
到目前为止,这是我的代码
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import cv2
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data() # 28x28 tal fra 0-9
x_train = tf.keras.utils.normalize(x_train, axis=1).reshape(x_train.shape[0], -1)
x_test = tf.keras.utils.normalize(x_test, axis=1).reshape(x_test.shape[0], -1)
model = tf.keras.models.Sequential()
#model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu, input_shape= x_train.shape[1:]))
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=3)
val_loss, val_acc = model.evaluate(x_test, y_test)
print (val_loss)
print (val_acc)
model.save('projekt_tal_laeser')
new_model = tf.keras.models.load_model('projekt_tal_laeser')
predictions = new_model.predict(x_test)
print(predictions)
print(np.argmax(predictions[0]))
然后在保存 model 之后,我制作了这段代码
import tensorflow as tf
import numpy as np
import cv2
def prepare(filepath):
global prediction, model
IMG_SIZE = 28
img_array = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE)
new_array = cv2.resize(img_array,(IMG_SIZE, IMG_SIZE))
return new_array.reshape(-1, IMG_SIZE, IMG_SIZE, 1)
model = tf.keras.models.load_model('projekt_tal_laeser')
prediction = model.predict([prepare('number.jpg')])
print(prediction)
我现在使用第二个代码遇到的错误是ValueError: Input 0 of layer sequence is incompatible with the layer: expected axis -1 of input shape to have value 784 but received input with shape [None, 28, 28, 1]
我制作第二个代码的原因是为了更容易将其实现到 GUI 中,这是我的第一个项目,非常感谢任何输入:)
解决方案:
prediction = model.predict([prepare('number.jpg').reshape(-1,784)])
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