AttributeError: 'ImageDataGenerator' object has no attribute 'shape'

Question

I am new in coding. I am trying to get the score but unfortunately i am getting errors. I was using first import.keras until it gave me when i wanted to evaluate the score and predict.The training model happened well, i have gotten no problem there.It is after that, when i was aboout to get the score of my model that i got as error ImageDataGnerator: Object has no 'ndim'. Then i looked for help and someone told me to use import.tensorflow.keras instead and i did it....

After training the model again,reaching that part in order to get the score and predict after i've gotten another error saying : ImageDataGenerator object has no attribute shapes and a warning saying : WARNING : tensorflow : Falling back from v2 loop because of error : Failed to find data* **adapter that can handle input : < class 'tensorflow.python.keras.preprocessing.image.ImageDataGenerator'> , < class 'NoneType'

This is some of the code below.

import numpy as np 
import tensorflow as tf 
import cv2
import sys 
import os
import matplotlib.pyplot as plt
from tensorflow import keras
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.optimizers import Adam 


image_width, image_height = 150,150

Epochs =10
batch_size=45
Steps_per_epoch=190
Validation_data=20
num_classes = len(map_characters)




model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same', input_shape= (image_height,image_width ,3)))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))

model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))

model.add(Conv2D(256, (3, 3), padding='same')) 
model.add(Activation('relu'))
model.add(Conv2D(256, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))

model.add(Flatten())
model.add(Dense(1024))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
opt = Adam(lr=0.01, decay=1e-6, )

model.summary()

model.compile(loss='categorical_crossentropy',
              optimizer='Adam',   
              metrics=['accuracy'])'''

train_datagen= ImageDataGenerator (
 rescale=1./255,
 shear_range=0.2,
 zoom_range=0.2,
 horizontal_flip=True)



test_datagen = ImageDataGenerator(rescale = 1./255) 

training_generator = train_datagen.flow_from_directory(
 train_data_dir, 
 target_size = (image_height, image_width),
 batch_size = batch_size, 
 class_mode = 'categorical')

validation_generator = test_datagen.flow_from_directory(
 validation_data_dir, 
 target_size = (image_height, image_width), 
 batch_size = batch_size, 
 class_mode = 'categorical') 



result=model.fit_generator(training_generator, 
                   steps_per_epoch=Steps_per_epoch,
                   epochs = Epochs, 
                   validation_data = validation_generator,
                   validation_steps=Validation_data) 

score = model.evaluate(test_datagen,
                  validation_generator,
                  batch_size=batch_size)

Answer 1

要评估生成器，您需要使用evaluate_generator ，而不是evaluate 。