ValueError: Error when checking input: expected dense_11_input to have 3 dimensions, but got array with shape (0, 1)

Question

我是机器学习的新手，python 我尝试对患者是否患有癌症进行分类。我从 https://github.com/fahomid/ML-Tensorflow-Medical-Image/blob/master/tensorflow-model.py 中找到了一段代码我有一个小数据集。训练集和测试集有两个患者目录，其中包含仅用于尝试的 dicom 文件。代码如下；

import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
import os
import pydicom
import numpy as np
import PIL

# Generate data from dicom file
dataset = [];
labels = [];
for root, dirs, files in os.walk("training_data/Cancer"):
    for file in files:
        if file.endswith(".dcm"):
            ds = pydicom.dcmread(os.path.join(root, file))
            dataset.append(ds.pixel_array)
            labels.append(1);

for root, dirs, files in os.walk("training_data/Normal"):
   for file in files:
        if file.endswith(".dcm"):
            ds = pydicom.dcmread(os.path.join(root, file))
            dataset.append(ds.pixel_array)
            labels.append(0)


dataset_size = len(dataset)
dataset = np.asarray(dataset)
labels = np.asarray(labels)

# create model
model = Sequential()
model.add(Dense(32, activation='tanh', input_shape=(512, 512)))
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics= 
['accuracy'])
model.fit(dataset, labels, epochs=10, shuffle=True, batch_size=32)

# serialize model to JSON
model_json = model.to_json()
with open("model.json", "w") as json_file:
    json_file.write(model_json)
# serialize weights to HDF5
model.save_weights("model.h5")
print("\n\nModel saved to disk\n\n")
model.summary()

报错行如下;

model.fit(dataset, labels, epochs=10, shuffle=True, batch_size=32)

ValueError: Error when checking input: expected dense_11_input to have 3 dimensions, but got array with shape (0, 1)

感谢您的帮助。

Answer 1

代码的以下部分查找包含癌症和正常数据的文件（扩展名为.dcm）人。它没有找到任何东西，所以它returns什么都没有。

# Generate data from dicom file
dataset = [];
labels = [];
for root, dirs, files in os.walk("training_data/Cancer"):
    for file in files:
        if file.endswith(".dcm"):
            ds = pydicom.dcmread(os.path.join(root, file))
            dataset.append(ds.pixel_array)
            labels.append(1);

for root, dirs, files in os.walk("training_data/Normal"):
   for file in files:
        if file.endswith(".dcm"):
            ds = pydicom.dcmread(os.path.join(root, file))
            dataset.append(ds.pixel_array)
            labels.append(0)

所以dataset变量的值为0，labels变量的值为1。当调用 model.fit 方法时，它期望输入是 3 维的，形状为 (512, 512)，但它只得到形状为 (0, 1).[=17= 的输入]

ValueError: Error when checking input: expected dense_11_input to have 3 dimensions, but got array with shape (0, 1)

ValueError: Error when checking input: expected dense_11_input to have 3 dimensions, but got array with shape (0, 1)

python

conv-neural-network

keras

valueerror