Python tf.Data.Dataset.from_tensor_切片的数据类型错误。。。无法将TensorShape转换为数据类型：<；数据类型：'；浮动32'&燃气轮机；

我试图从csv中获取数据，其中包含一个文件列表和一个标签列表，并使用tf.keras将其转换为一个热标签进行分类。我对代码使用了渴望模式

我试图按照CS230中的tf.data示例构建数据管道

我的代码在下面的代码部分

列出所有图片位置的csv文件位于dropbox上：

当我运行如下所示的代码时，我得到一个

TypeError: Cannot convert a TensorShape to dtype: <dtype: 'float32'>
error.

我得到这个错误：

InvalidArgumentError: cannot compute Mul as input #0 was expected to be 
a float tensor but is a int32 tensor [Op:Mul] 
name: loss/activation_2_loss/mul/

当我跑的时候

iterator.get_next()

这些图片的格式如下所示

<tf.Tensor: id=12462, shape=(32, 300, 300, 3), dtype=float32, numpy=

我在跑步： tf:1.10.0 Python:3.6.7 | Anaconda custom（64位）|（默认值，2018年12月10日，20:35:02）[MSC v.1915 64位（AMD64）]

我不知道这是否真的是解决方案，但当我切换时：

fitting = model.fit_generator(iterator,epochs =1 ,shuffle=False, steps_per_epoch=14400//batch_size)

到

---

模型确实开始训练了。但是，由于迭代器将不会重新启动，因此模型将耗尽数据点

你的问题现在解决了吗？如果将Tensorflow升级到1.14，错误将消失。

 <tf.Tensor: id=12463, shape=(32, 13), dtype=float32, numpy=

import pandas as pd
import tensorflow as tf
import tensorflow.keras as k
#import cv2
#tf.enable_eager_execution()
#import argparse
#from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense



def parse_function(filename, label):
    image_string = tf.read_file(filename)

    # Don't use tf.image.decode_image, or the output shape will be undefined
    image = tf.image.decode_jpeg(image_string, channels=3)

    # This will convert to float values in [0, 1]
    image = tf.image.convert_image_dtype(image, tf.float32)


    image = tf.image.resize_images(image, [300, 300])
    return image, label



def train_preprocess(image, label):
    image = tf.image.random_flip_left_right(image)

    image = tf.image.random_brightness(image, max_delta=32.0 / 255.0)
    image = tf.image.random_saturation(image, lower=0.5, upper=1.5)

    # Make sure the image is still in [0, 1]
    image = tf.clip_by_value(image, 0.0, 1.0)

    return image, label

batch_size = 32
classes = 13

fileLoc = "C:/Users/USAgData/TF/Clock.csv"
file = pd.read_csv(fileLoc)
file['Loc']=''
file.Loc = str(str(file.Location)[9:23] + str(file.Location)[28:46])


one_hot_Hr = tf.one_hot(file.Hr,classes)
#one_hot_Hr = tf.to_int32(one_hot_Hr)



dataset = tf.data.Dataset.from_tensor_slices((file.Loc, one_hot_Hr))
dataset = dataset.shuffle(len(file.Location))
dataset = dataset.map(parse_function, num_parallel_calls=4)
dataset = dataset.map(train_preprocess, num_parallel_calls=4)
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(1)

#print(dataset.shape) # ==> "(tf.float32, tf.float32)"

iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()

#print(next_element)

tf.keras.backend.clear_session()

model_name="Documentation"
model = k.Sequential()
model.add(Conv2D(64, (3, 3), input_shape=(300,300,3))) #Changed shape to include batch
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

#model.add(Conv2D(32, (3, 3)))
#model.add(Activation('relu'))
#model.add(MaxPooling2D(pool_size=(2, 2)))

#model.add(Conv2D(64, (3, 3)))
#model.add(Activation('relu'))
#model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(32))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(classes))
model.add(Activation('softmax')) #Changed from sigmoid




#changed from categorical cross entropy
model.compile(loss='categorical_crossentropy',
              optimizer=tf.train.RMSPropOptimizer(.0001),
              metrics=['accuracy'])

model.summary()



fitting = model.fit_generator(iterator,epochs =1 ,shuffle=False, steps_per_epoch=14400//batch_size)


#model.evaluate(dataset,steps=30)


import sys
print(sys.version)
tf.__version__

fitting = model.fit_generator(iterator,epochs =1 ,shuffle=False, steps_per_epoch=14400//batch_size)

fitting = model.fit(iterator,epochs = 1 , shuffle = False, steps_per_epoch = 14400//batch_size)