autoenc3 = tf.keras.models.Sequential(name = 'Auto-Encoder_3')
#encode
autoenc3.add(ZeroPadding2D((1,1),input_shape = (254,254,3) ))
autoenc3.add(Conv2D(8, 3, padding = 'same' ))
autoenc3.add(LeakyReLU())
autoenc3.add(MaxPooling2D(pool_size = (2,2), strides = 2))
autoenc3.add(Conv2D(16, 3, padding = 'same'))
autoenc3.add(LeakyReLU())
autoenc3.add(MaxPooling2D(pool_size = (2,2), strides = 2))
autoenc3.add(Conv2D(32, 3, padding = 'same'))
autoenc3.add(LeakyReLU())
autoenc3.add(MaxPooling2D(pool_size = (2,2), strides = 2))
#decode
autoenc3.add(UpSampling2D(2))
autoenc3.add(Conv2D(16, 1, padding = 'same'))
autoenc3.add(LeakyReLU())
autoenc3.add(UpSampling2D(2))
autoenc3.add(Conv2D(8, 1, padding = 'same'))
autoenc3.add(LeakyReLU())
autoenc3.add(UpSampling2D(2))
autoenc3.add(Conv2D(3, 1, activation = 'sigmoid', padding = 'same'))
autoenc3.add(Cropping2D(cropping=((1, 1), (1, 1))))
autoenc3.compile(loss = 'mse', optimizer = Adam(lr = 0.0001))
I got an error after this code:
AttributeError: module 'tensorflow.python.training.experimental.mixed_precision' has no attribute '_register_wrapper_optimizer_cls'
I'm checking and upgrading my Keras and TensorFlow packages but still, I'm taking the same error.
my imports here:
import os
from pathlib import Path
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import sklearn
import scipy
import statsmodels.api as sm
import random
from skimage.io import imread as imread
from skimage.util import montage
import cv2
from skimage.color import rgb2gray
from PIL import Image, ImageFilter, ImageOps
import PIL
from PIL import Image
from sklearn import metrics
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import cross_val_predict
from sklearn.metrics import f1_score
from sklearn.model_selection import GridSearchCV, KFold
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_validate
from sklearn.metrics import f1_score
from sklearn.metrics import average_precision_score
from sklearn.metrics import precision_recall_curve
from matplotlib import pyplot
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
from sklearn import svm
# Importing keras library for creating Convolution Neural Network(CNN)
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import Conv2D, UpSampling2D, InputLayer, Conv2DTranspose, Activation, Dense,Dropout, Flatten, BatchNormalization,Reshape, ZeroPadding2D, Cropping2D
from tensorflow.keras.layers import MaxPooling2D, Input
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping
from tensorflow.keras.preprocessing.image import array_to_img, img_to_array, load_img
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.layers import LeakyReLU
from keras.wrappers.scikit_learn import KerasClassifier
import math
# libraries for file operations
import os
import pathlib
import warnings
warnings.filterwarnings('ignore')
