CIFAR-10 硬维度错误

Question

我正在尝试运行我机器 GPU 中的 Cifar-10 CNN 代码，但我面临以下问题：

维度 (-1) 必须在 [0, 2) 范围内，其中 2 是输入中的维度数。对于 'metrics/acc/ArgMax' (op: 'ArgMax') 输入形状：[?,?], [].

这是我的代码：

import os
os.environ["THEANO_FLAGS"] = "mode=FAST_RUN,device=cuda0,floatX=float32,lib.cnmem=1"
import theano
from __future__ import print_function
import keras
from keras.datasets import cifar10
from keras import optimizers
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D

from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())

[名称: "/cpu:0" device_type: "CPU" memory_limit：268435456 地点{ } 化身：5668889307863094193 ，名称：“/ GPU：0” device_type: "GPU" memory_limit: 1563603763 地点{ bus_id: 1 } 化身：18418621293925924623 physical_device_desc: "device: 0, name: GeForce GTX 960M, pci bus id: 0000:01:00.0" ]

import os
import pickle
import numpy as np

batch_size = 32
num_classes = 10
epochs = 200
data_augmentation = True
num_predictions = 20
save_dir = os.path.join(os.getcwd(), 'saved_models')
model_name = 'keras_cifar10_trained_model.h5'

# The data, shuffled and split between train and test sets:
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
#x_train = x_train.reshape(50000, 3072)
#x_test = x_test.reshape(10000, 3072)
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

x_train 形状：(50000, 32, 32, 3) 50000 个训练样本 10000 个测试样本

# Convert class vectors to binary class matrices.
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()

model.add(Conv2D(32, (3, 3), padding='same',
                 input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(Activation('softmax'))

model.summary()

Layer (type)                 Output Shape              Param #   

conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       
_________________________________________________________________
activation_1 (Activation)    (None, 32, 32, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 30, 30, 32)        9248      
_________________________________________________________________
activation_2 (Activation)    (None, 30, 30, 32)        0         
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 15, 15, 32)        0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 15, 15, 32)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 15, 15, 64)        18496     
_________________________________________________________________
activation_3 (Activation)    (None, 15, 15, 64)        0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 13, 13, 64)        36928     
_________________________________________________________________
activation_4 (Activation)    (None, 13, 13, 64)        0         
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 6, 6, 64)          0         
_________________________________________________________________
dropout_2 (Dropout)          (None, 6, 6, 64)          0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 2304)              0         
_________________________________________________________________
dense_1 (Dense)              (None, 512)               1180160   
_________________________________________________________________
activation_5 (Activation)    (None, 512)               0         
_________________________________________________________________
dropout_3 (Dropout)          (None, 512)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 10)                5130      
_________________________________________________________________
activation_6 (Activation)    (None, 10)                0         

Total params: 1,250,858
Trainable params: 1,250,858
Non-trainable params: 0

# initiate RMSprop optimizer
opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)
# Let's train the model using RMSprop
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])

在执行 RMSprop 优化器代码时出现以下错误：

InvalidArgumentError Traceback (most recent call last) ~\Anaconda3\lib\site-packages\tensorflow\python\framework\common_shapes.py in _call_cpp_shape_fn_impl(op, input_tensors_needed, input_tensors_as_shapes_needed, debug_python_shape_fn, require_shape_fn) 669 node_def_str, input_shapes, input_tensors, input_tensors_as_shapes, --> 670 status) 671 except errors.InvalidArgumentError as err:

。 . . . .

ValueError: Dimension (-1) must be in the range [0, 2), where 2 is the number of dimensions in the input. for 'metrics/acc/ArgMax' (op: 'ArgMax') with input shapes: [?,?], [].

在查看其他线程后，我尝试了两种不同的解决方案，但问题仍然存在。建议的第一个解决方案是更新 Tensorflow。第二种解决方案是将训练和测试数据从 x_train shape: (50000, 32, 32, 3) 重塑为 x_train shape: (50000, 3072)，但随后面临的错误是无法使用重塑数据的模型 Conv2D 层。

谁能帮我解决这个问题？任何帮助表示赞赏。

Answer 1

我重新安装 Anaconda、Tensorflow 和 Keras 后问题得到解决

CIFAR-10 硬维度错误

CIFAR-10 Dimension Error Keras

gpu

deep-learning

keras

keras-layer