AttributeError: module 'tensorflow' has no attribute 'placeholder'

• 问题原因
• 解决方法
• 参考

问题原因

使用了与当前tensorflow版本不匹配的方法

解决方法

1.Tensorflow 团队提供的解决方案

import tensorflow.compat.v1 as tf
tf.disable_v2_behavior() 

2.重新下载对应2.0以下版本的tensorflow
3.调整为新的使用方法，这里以基于mnist的自编码器重构例子来说明解决方法

from keras.layers import Input, Dense
from keras.models import Model
from keras.datasets import mnist
import numpy as np

# this is the size of our encoded representations
encoding_dim = 32  # 32 floats -> compression of factor 24.5, assuming the input is 784 floats

# this is our input placeholder
input_img = Input(shape=(784,))
# "encoded" is the encoded representation of the input
encoded = Dense(encoding_dim, activation='relu')(input_img)
# "decoded" is the lossy reconstruction of the input
decoded = Dense(784, activation='sigmoid')(encoded)

# this model maps an input to its reconstruction
autoencoder = Model(input=input_img, output=decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')

(x_train, _), (x_test, _) = mnist.load_data()

x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
print x_train.shape
print x_test.shape

autoencoder.fit(x_train, x_train,
                nb_epoch=50,
                batch_size=256,
                shuffle=True,
                validation_data=(x_test, x_test))

将模型以及mnist的导入方法调整后

import tensorflow as tf
import tensorflow.keras as keras
import tensorflow.keras.layers as layers
import numpy as np
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

# this is the size of our encoded representations
encoding_dim = 32  # 32 floats -> compression of factor 24.5, assuming the input is 784 floats

# this is our input placeholder
input_img = layers.Input(shape=(784,))
# "encoded" is the encoded representation of the input
encoded = layers.Dense(encoding_dim, activation='relu')(input_img)
# "decoded" is the lossy reconstruction of the input
decoded = layers.Dense(784, activation='sigmoid')(encoded)

# this model maps an input to its reconstruction
autoencoder = tf.keras.Model(input_img,decoded)

autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')

(x_train, _), (x_test, _) = keras.datasets.mnist.load_data()

x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
print (x_train.shape)
print (x_test.shape)

autoencoder.fit(x_train, x_train,
                nb_epoch=50,
                batch_size=256,
                shuffle=True,
                validation_data=(x_test, x_test))

#E7.11 poch 50/50,60000/60000 [==============================] - 2s 41us/sample - loss: 0.6835 - val_loss: 0.6832

参考

1.https://github.com/theislab/scgen/issues/14