重剑无锋博客

import osimport cv2from DeepFace import functions,analyzefrom keras.callbacks import ModelCheckpointimport numpy as npfrom sklearn import metricsimport tensorflow as tffrom sklearn.utils.class_weight import compute_class_weight, compute_sample_weig

假设img为PIL.image格式img = tf.convert_to_tensor(img)img = tf.image.resize(img,(224,224))

import tensorflow as tftf_version = int(tf.__version__.split(".")[0])if tf_version == 1: from keras.models import Model, Sequential from keras.layers import Input, Convolution2D, ZeroPadding2D, MaxPooling2D, Flatten, Dense, Dropout, Activationelse:

h5py和tensorflow版本不对应问题

用import tensorflow.keras替代import keras

tf.keras.layers.Conv1D(filters, kernel_size, strides=1, padding=‘valid’,data_format=‘channels_last’,dilation_rate=1, groups=1, activation=None, use_bias=True,kernel_initializer=‘glorot_uniform’, bias_initializer=‘zeros’,kernel_regularizer=None, bias_r

model = Sequential()model.add(LSTM(units=hidden, input_shape=(time_steps, input_size),return_sequences=True))#返回所有节点的输出model.add(LSTM(units=hidden,return_sequences=False))#返回最后一个节点的输出### model.add(Dense(1, activation='softmax'))model.add(Dense(1, acti

from tensorflow.keras.layers import Dense, LSTMfrom tensorflow.keras.models import Sequentialimport pandas as pdimport numpy as npdf = pd.read_excel(r'C:\Users\zhoutao\Desktop\data\data01.xlsx')# ###########df=df.drop(['dropcol'][0], axis=1)X=df.dr

from keras.layers import Dense, LSTMfrom keras.models import Sequential改为from tensorflow.keras.layers import Dense, LSTMfrom tensorflow.keras.models import Sequential

tensorflow2.0以上版本将tf.enable_eager_execution()改为tf.compat.v1.enable_eager_execution()

将tf.train.Optimizer改为tf.keras.optimizers.Optimizer

config = tf.ConfigProto()改为config = tf.compat.v1.ConfigProto()session = tf.Session(config=config)改为session = tf.compat.v1.Session(config=config)

from keras.layersimport activations改为from keras import activations

将相对引用改为绝对引用from . import wikitext改为from example import wikitext

pip install --upgrade tensorflow