如何将skimage imread_collection通过tensorflow

Question

我正在尝试通过神经网络放置图像集合，但我无法弄清楚如何将大量图像集合放入张量流模型中，因为我试图将集合转换为numpy 数组导致内存错误。

请注意，我是 tensorflow 的新手。

import numpy as np
from skimage.io import imread_collection
from tensorflow import keras
from tensorflow.keras import layers

def gen(arr):return(i.reshape(400*600*3) for i in arr) # Only used in Attempt2.

labelFile=open("lables_text_file.txt","r")
labels=labelFile.read()
labelFile.close()
labels=getTrain(labels)#Converts to a tuple containing the lables in order.

data = imread_collection("path_to_images/*.jpg", conserve_memory=True)
train=data[:-len(data)//4]
trainLabels=labels[:-len(data)//4]
test=data[-len(data)//4:]
testLabels=labels[-len(data)//4:]

#train = train.reshape(-1, 400*600*3) # Attempt1
#test = test.reshape(-1, 400*600*3) # Attempt1
#train = gen(train) # Attempt2
#test = gen(test) # Attempt2
trainLabels = keras.utils.to_categorical(trainLabels, 23)
testLabels = keras.utils.to_categorical(testLabels, 23)

model=keras.Sequential([keras.Input(shape=(400*600*3,)),
        layers.Dense(600, name='hidden1', activation='relu'),
        layers.Dense(400, name='hidden2', activation='relu'),
        layers.Dense(46, name='hidden3', activation='relu'),
        layers.Dense(23, activation="softmax")])

optimizer = keras.optimizers.Adam(learning_rate=0.0015)
model.compile(loss=keras.losses.CategoricalCrossentropy(), optimizer=optimizer, metrics=[keras.metrics.CategoricalAccuracy()])
model.fit(train,trainLabels,batch_size=128,epochs=8,validation_data=(test,testLabels), shuffle=True)

当我按原样运行代码时，结果如下：

ValueError: Failed to find data adapter that can handle input: <class 'skimage.io.collection.ImageCollection'>, <class 'numpy.ndarray'>

当我尝试使用 Attempt1 时，结果如下：

AttributeError: 'ImageCollection' object has no attribute 'reshape'

当我尝试使用 Attempt2 时，结果如下：

ValueError: `y` argument is not supported when using python generator as input.

如何将数据放入`model.fit，以便成功训练神经网络？

Answer 1

我想我可能已经解决了问题。

工作代码：

import numpy as np
from skimage.io import imread_collection
from tensorflow import keras
from tensorflow.keras import layers

def gen(arr,labels):return((arr[i].reshape(-1,400*600*3),labels[i].reshape(-1,23)) for i in range(len(arr)))

labelFile=open("lables_text_file.txt","r")
labels=labelFile.read()
labelFile.close()
labels=getTrain(labels)#Converts to a tuple containing the lables in order.

data = imread_collection("path_to_images/*.jpg", conserve_memory=True)
train=data[:-len(data)//4]
trainLabels=labels[:-len(data)//4]
test=data[-len(data)//4:]
testLabels=labels[-len(data)//4:]

#train = train.reshape(-1, 400*600*3) # Attempt1
#test = test.reshape(-1, 400*600*3) # Attempt1
trainLabels = keras.utils.to_categorical(trainLabels, 23)
testLabels = keras.utils.to_categorical(testLabels, 23)
train = gen(train,trainLabels) # Attempt2
test = gen(test,testLabels) # Attempt2

model=keras.Sequential([keras.Input(shape=(400*600*3,)),
        layers.Dense(600, name='hidden1', activation='relu'),
        layers.Dense(400, name='hidden2', activation='relu'),
        layers.Dense(46, name='hidden3', activation='relu'),
        layers.Dense(23, activation="softmax")])

optimizer = keras.optimizers.Adam(learning_rate=0.0015)
model.compile(loss=keras.losses.CategoricalCrossentropy(), optimizer=optimizer, metrics=[keras.metrics.CategoricalAccuracy()])
model.fit(train,None,batch_size=128,epochs=8,validation_data=(test,testLabels), shuffle=True)

解决方案是传递一个包含输入和标签的 returns 二元组的生成器（而不是直接传递标签），但是如果有其他问题我可能会包含在这个答案中我有时间了。

如何将skimage imread_collection通过tensorflow

How to put skimage imread_collection through tensorflow

python

scikit-image

tensorflow