snlpatel001213 / algorithmia Goto Github PK

folderName = "imdb_crop_resized128*128" 
fileNames =  glob.glob(folderName+"/*.jpg")
NumberOfFileToBetested = 500

x_batch,y_batch = turnToNumpy(fileNames[:NumberOfFileToBetested])

predictedAGE = model.predict_classes(x_batch)

realAGE = []
imagesCount = 0
os.mkdir('testImages')

for eachFilename in fileNames[:NumberOfFileToBetested]:
    realAGE = getRealAge(eachFilename)
    #saving Images
    x = load_image(eachFilename)
    x = x.reshape(x.shape[1],x.shape[2],x.shape[0])
    renamedImage = "testImages/"+str(realAGE)+"_"+str(predictedAGE[imagesCount])+".jpg"                                 
    imsave(renamedImage, x)
    imagesCount = imagesCount + 1

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-17-7f653efe2f7b> in <module>()
      7 x_batch,y_batch = turnToNumpy(fileNames[:NumberOfFileToBetested])
      8 
----> 9 predictedAGE = model.predict_classes(x_batch)
     10 
     11 realAGE = []

~/Téléchargements/anaconda3/lib/python3.6/site-packages/keras/models.py in predict_classes(self, x, batch_size, verbose)
    824             A numpy array of class predictions.
    825         '''
--> 826         proba = self.predict(x, batch_size=batch_size, verbose=verbose)
    827         if proba.shape[-1] > 1:
    828             return proba.argmax(axis=-1)

~/Téléchargements/anaconda3/lib/python3.6/site-packages/keras/models.py in predict(self, x, batch_size, verbose)
    714         if self.model is None:
    715             self.build()
--> 716         return self.model.predict(x, batch_size=batch_size, verbose=verbose)
    717 
    718     def predict_on_batch(self, x):

~/Téléchargements/anaconda3/lib/python3.6/site-packages/keras/engine/training.py in predict(self, x, batch_size, verbose)
   1199         x = standardize_input_data(x, self.input_names,
   1200                                    self.internal_input_shapes,
-> 1201                                    check_batch_dim=False)
   1202         if self.stateful:
   1203             if x[0].shape[0] > batch_size and x[0].shape[0] % batch_size != 0:

~/Téléchargements/anaconda3/lib/python3.6/site-packages/keras/engine/training.py in standardize_input_data(data, names, shapes, check_batch_dim, exception_prefix)
     99                                  ' to have ' + str(len(shapes[i])) +
    100                                  ' dimensions, but got array with shape ' +
--> 101                                  str(array.shape))
    102             for j, (dim, ref_dim) in enumerate(zip(array.shape, shapes[i])):
    103                 if not j and not check_batch_dim:

ValueError: Error when checking : expected zeropadding2d_input_1 to have 4 dimensions, but got array with shape (0, 1)