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同樣程式在anaconda中OK，但在COLAB中有以下問題:
import numpy as np
np.random.seed(10)
import matplotlib.pyplot as plt
from keras.models import load_model
import glob,cv2
def show_images_labels_predictions(images,labels,
predictions,start_id,num=10):
plt.gcf().set_size_inches(12, 14)
if num>25: num=25
for i in range(0, num):
ax=plt.subplot(5,5, 1+i)
#顯示黑白圖片
ax.imshow(images[start_id], cmap='binary')
# 有 AI 預測結果資料, 才在標題顯示預測結果
if( len(predictions) > 0 ) :
title = 'ai = ' + str(predictions[i])
# 預測正確顯示(o), 錯誤顯示(x)
title += (' (o)' if predictions[i]==labels[i] else ' (x)')
title += '\nlabel = ' + str(labels[i])
# 沒有 AI 預測結果資料, 只在標題顯示真實數值
else :
title = 'label = ' + str(labels[i])
# X, Y 軸不顯示刻度
ax.set_title(title,fontsize=12)
ax.set_xticks([]);ax.set_yticks([])
start_id+=1
plt.show()
#建立測試特徵集、測試標籤
files = glob.glob("/content/drive/MyDrive/Colab Notebooks/機器學習與深度學習特訓班/imagedata\*.jpg" )
test_feature=[]
test_label=[]
for file in files:
img=cv2.imread(file)
img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) #灰階
_, img = cv2.threshold(img, 120, 255, cv2.THRESH_BINARY_INV) #轉為反相黑白
test_feature.append(img)
label=file[10:11] # "imagedata\1.jpg" 第10個字元1為label
test_label.append(int(label))
test_feature=np.array(test_feature) # 串列轉為矩陣
test_label=np.array(test_label) # 串列轉為矩陣
#將 Features 特徵值換為 784個 float 數字的 1 維向量
test_feature_vector = test_feature.reshape(len( test_feature), 784).astype('float32')
#Features 特徵值標準化
test_feature_normalize = test_feature_vector/255
#從 HDF5 檔案中載入模型
print("載入模型 Mnist_mlp_model.h5")
model = load_model('Mnist_mlp_model.h5')
#預測
#prediction=model.predict_classes(test_feature_normalize)#已棄用
predict=model.predict(test_feature_normalize)
prediction=np.argmax(predict,axis=1)
#顯示圖像、預測值、真實值
show_images_labels_predictions(test_feature,test_label,prediction,0,len(test_feature))
載入模型 Mnist_mlp_model.h5
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-16-de705c155693> in <module>()
57 #預測
58 #prediction=model.predict_classes(test_feature_normalize)#已棄用
---> 59 predict=model.predict(test_feature_normalize)
60 prediction=np.argmax(predict,axis=1)
61
1 frames
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
1995 callbacks.on_predict_batch_end(end_step, {'outputs': batch_outputs})
1996 if batch_outputs is None:
-> 1997 raise ValueError('Unexpected result of `predict_function` '
1998 '(Empty batch_outputs). Please use '
1999 '`Model.compile(..., run_eagerly=True)`, or '
ValueError: Unexpected result of `predict_function` (Empty batch_outputs). Please use `Model.compile(..., run_eagerly=True)`, or `tf.config.run_functions_eagerly(True)` for more information of where went wrong, or file a issue/bug to `tf.keras`. |
機器學習與深度學習特訓班
