我尝试用本地图像替换训练和验证数据。但是在运行训练代码时，它出现了错误：
ValueError：无法压缩dim [1]，预期维度为1，对于'sparse_softmax_cross_entropy_loss / remove_squeezable_dimensions / Squeeze'（op：'Squeeze'）得到3，输入形状为：[100,3]。
我不知道如何修复它。模型定义代码中没有可见变量。代码是从TensorFlow教程修改的。图像是jpgs。
以下是详细错误消息：
INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_log_step_count_steps': 100, '_is_chief': True, '_model_dir': '/tmp/mnist_convnet_model', '_tf_random_seed': None, '_session_config': None, '_save_checkpoints_secs': 600, '_num_worker_replicas': 1, '_save_checkpoints_steps': None, '_service': None, '_keep_checkpoint_max': 5, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x00000288088D50F0>, '_keep_checkpoint_every_n_hours': 10000, '_task_type': 'worker', '_master': '', '_save_summary_steps': 100, '_num_ps_replicas': 0, '_task_id': 0}
Traceback (most recent call last):
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 686, in _call_cpp_shape_fn_impl
input_tensors_as_shapes, status)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 473, in __exit__
c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Can not squeeze dim[1], expected a dimension of 1, got 3 for 'sparse_softmax_cross_entropy_loss/remove_squeezable_dimensions/Squeeze' (op: 'Squeeze') with input shapes: [100,3].
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "D:\tf_exe_5_make_image_lables\cnn_mnist.py", line 214, in <module>
tf.app.run()
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\platform\app.py", line 124, in run
_sys.exit(main(argv))
File "D:\tf_exe_5_make_image_lables\cnn_mnist.py", line 203, in main
hooks=[logging_hook])
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\estimator\estimator.py", line 314, in train
loss = self._train_model(input_fn, hooks, saving_listeners)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\estimator\estimator.py", line 743, in _train_model
features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\estimator\estimator.py", line 725, in _call_model_fn
model_fn_results = self._model_fn(features=features, **kwargs)
File "D:\tf_exe_5_make_image_lables\cnn_mnist.py", line 67, in cnn_model_fn
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\losses\losses_impl.py", line 790, in sparse_softmax_cross_entropy
labels, logits, weights, expected_rank_diff=1)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\losses\losses_impl.py", line 720, in _remove_squeezable_dimensions
labels, predictions, expected_rank_diff=expected_rank_diff)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\confusion_matrix.py", line 76, in remove_squeezable_dimensions
labels = array_ops.squeeze(labels, [-1])
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\array_ops.py", line 2490, in squeeze
return gen_array_ops._squeeze(input, axis, name)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 7049, in _squeeze
"Squeeze", input=input, squeeze_dims=axis, name=name)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 3162, in create_op
compute_device=compute_device)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 3208, in _create_op_helper
set_shapes_for_outputs(op)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 2427, in set_shapes_for_outputs
return _set_shapes_for_outputs(op)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 2400, in _set_shapes_for_outputs
shapes = shape_func(op)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 2330, in call_with_requiring
return call_cpp_shape_fn(op, require_shape_fn=True)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 627, in call_cpp_shape_fn
require_shape_fn)
File "C:\Users\ASUS\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 691, in _call_cpp_shape_fn_impl
raise ValueError(err.message)
ValueError: Can not squeeze dim[1], expected a dimension of 1, got 3 for 'sparse_softmax_cross_entropy_loss/remove_squeezable_dimensions/Squeeze' (op: 'Squeeze') with input shapes: [100,3].
>>>
这是我的代码：
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
#imports
import numpy as np
import tensorflow as tf
import glob
import cv2
import random
import matplotlib.pylab as plt
import pandas as pd
import sys as system
from mlxtend.preprocessing import one_hot
from sklearn import preprocessing
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
tf.logging.set_verbosity(tf.logging.INFO)
def cnn_model_fn(features, labels, mode):
"""Model function for CNN"""
#Input Layer
input_layer = tf.reshape(features["x"], [-1,320,320,3])
#Convolutional Layer #1
conv1 = tf.layers.conv2d(
inputs = input_layer,
filters = 32,
kernel_size=[5,5],
padding = "same",
activation=tf.nn.relu)
#Pooling Layer #1
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2,2], strides=2)
#Convolutional Layer #2 and Pooling Layer #2
conv2 = tf.layers.conv2d(
inputs=pool1,
filters=64,
kernel_size=[5,5],
padding="same",
activation=tf.nn.relu)
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2,2], strides=2)
#Dense Layer
pool2_flat = tf.reshape(pool2, [-1,80*80*64])
dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
dropout = tf.layers.dropout(
inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)
#Logits Layer
logits = tf.layers.dense(inputs=dropout, units=3)
predictions = {
#Generate predictions (for PREDICT and EVAL mode)
"classes": tf.argmax(input=logits, axis=1),
#Add 'softmax_tensor' to the graph. It is used for PREDICT and by the
#'logging_hook'
"probabilities": tf.nn.softmax(logits, name="softmax_tensor")
}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
# Calculate Loss (for both TRAIN and EVAL modes
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
# Configure the Training Op (for TRAIN mode)
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
train_op = optimizer.minimize(
loss=loss,
global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
# Add evaluation metrics (for EVAL mode)
eval_metric_ops = {
"accuracy": tf.metrics.accuracy(
labels=labels, predictions=predictions["classes"])}
return tf.estimator.EstimatorSpec(
mode=mode, loss=loss,eval_metric_ops=eval_metric_ops)
def main(unused_argv):
'''
#Load training and eval data
mnist = tf.contrib.learn.datasets.load_dataset("mnist")
train_data = mnist.train.images
train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
eval_data = mnist.test.images
eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)
'''
#Load cats, dogs and cars image in local folder
X_data = []
files = glob.glob("data/cats/*.jpg")
for myFile in files:
image = cv2.imread (myFile)
imgR = cv2.resize(image, (320, 320))
imgNR = imgR/255
X_data.append(imgNR)
files = glob.glob("data/dogs/*.jpg")
for myFile in files:
image = cv2.imread (myFile)
imgR = cv2.resize(image, (320, 320))
imgNR = imgR/255
X_data.append(imgNR)
files = glob.glob ("data/cars/*.jpg")
for myFile in files:
image = cv2.imread (myFile)
imgR = cv2.resize(image, (320, 320))
imgNR = imgR/255
X_data.append (imgNR)
#print('X_data count:', len(X_data))
X_data_Val = []
files = glob.glob ("data/Validation/cats/*.jpg")
for myFile in files:
image = cv2.imread (myFile)
imgR = cv2.resize(image, (320, 320))
imgNR = imgR/255
X_data_Val.append (imgNR)
files = glob.glob ("data/Validation/dogs/*.jpg")
for myFile in files:
image = cv2.imread (myFile)
imgR = cv2.resize(image, (320, 320))
imgNR = imgR/255
X_data_Val.append (imgNR)
files = glob.glob ("data/Validation/cars/*.jpg")
for myFile in files:
image = cv2.imread (myFile)
imgR = cv2.resize(image, (320, 320))
imgNR = imgR/255
X_data_Val.append (imgNR)
#Feed One hot lables
Y_Label = np.zeros(shape=(300,1))
for el in range(0,100):
Y_Label[el]=[0]
for el in range(101,200):
Y_Label[el]=[1]
for el in range(201,300):
Y_Label[el]=[2]
onehot_encoder = OneHotEncoder(sparse=False)
#Y_Label_RS = Y_Label.reshape(len(Y_Label), 1)
Y_Label_Encode = onehot_encoder.fit_transform(Y_Label)
#print('Y_Label_Encode shape:', Y_Label_Encode.shape)
Y_Label_Val = np.zeros(shape=(30,1))
for el in range(0, 10):
Y_Label_Val[el]=[0]
for el in range(11, 20):
Y_Label_Val[el]=[1]
for el in range(21, 30):
Y_Label_Val[el]=[2]
#Y_Label_Val_RS = Y_Label_Val.reshape(len(Y_Label_Val), 1)
Y_Label_Val_Encode = onehot_encoder.fit_transform(Y_Label_Val)
#print('Y_Label_Val_Encode shape:', Y_Label_Val_Encode.shape)
train_data = np.array(X_data)
train_data = train_data.astype(np.float32)
train_labels = np.asarray(Y_Label_Encode, dtype=np.int32)
eval_data = np.array(X_data_Val)
eval_data = eval_data.astype(np.float32)
eval_labels = np.asarray(Y_Label_Val_Encode, dtype=np.int32)
print(train_data.shape)
print(train_labels.shape)
#Create the Estimator
mnist_classifier = tf.estimator.Estimator(
model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model")
# Set up logging for predictions
tensor_to_log = {"probabilities": "softmax_tensor"}
logging_hook = tf.train.LoggingTensorHook(
tensors=tensor_to_log, every_n_iter=50)
# Train the model
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": train_data},
y=train_labels,
batch_size=100,
num_epochs=None,
shuffle=True)
mnist_classifier.train(
input_fn=train_input_fn,
#original steps are 20000
steps=1,
hooks=[logging_hook])
# Evaluate the model and print results
eval_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": eval_data},
y=eval_labels,
num_epochs=1,
shuffle=False)
eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
print(eval_results)
if __name__ == "__main__":
tf.app.run()
答案 0 :(得分：22)
此处的错误来自 tf.losses.sparse_softmax_cross_entropy（labels = labels，logits = logits）。
TensorFlow文档明确指出“标签向量必须为每行logits的真实类提供单个特定索引”。所以你的标签向量必须只包括像0,1,2这样的类索引，而不是像[1,0,0]，[0,1,0]，[0,0,1]这样的单一热编码。
重现错误以进一步解释：
import numpy as np
import tensorflow as tf
# Create random-array and assign as logits tensor
np.random.seed(12345)
logits = tf.convert_to_tensor(np.random.sample((4,4)))
print logits.get_shape() #[4,4]
# Create random-labels (Assuming only 4 classes)
labels = tf.convert_to_tensor(np.array([2, 2, 0, 1]))
loss_1 = tf.losses.sparse_softmax_cross_entropy(labels, logits)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
print 'Loss: {}'.format(sess.run(loss_1)) # 1.44836854
# Now giving one-hot-encodings in place of class-indices for labels
wrong_labels = tf.convert_to_tensor(np.array([[0,0,1,0], [0,0,1,0], [1,0,0,0],[0,1,0,0]]))
loss_2 = tf.losses.sparse_softmax_cross_entropy(wrong_labels, logits)
# This should give you a similar error as soon as you define it
因此，尝试在Y_Labels向量中给出类索引而不是单热编码。 希望这能清除你的怀疑。
答案 1 :(得分：4)
更改
loss='sparse_categorical_crossentropy'
到
loss='categorical_crossentropy'
为我工作。
答案 2 :(得分：3)
我已经解决了这个错误。标签采用onehot编码，因此尺寸为[,10]，而不是[,1]。所以我使用了tf.argmax()。
答案 3 :(得分：1)
简单来说，如果您已将 labelbinarizer（用于热编码）应用于测试数据，则您的损失函数应该是 categorical_crossentropy。如果您尚未对测试数据进行热编码，则应使用'sparse_categorical_crossentropy'。
答案 4 :(得分：1)
您可以将一个热编码更改为 loss='categorical_crossentropy' 或前面提到的另一个选项是 tf.losses.sparse_softmax_cross_entropy(labels, logits),
答案 5 :(得分：0)
如果您使用Keras的ImageDataGenerator，则可以添加class_mode="sparse"以获取正确的级别：
train_datagen = keras.preprocessing.image.ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
train_generator = train_datagen.flow_from_directory(
'data/train',
target_size=(150, 150),
batch_size=32,
class_mode="sparse")
或者，您也许可以使用softmax_cross_entropy，它似乎对标签使用onehot编码。
答案 6 :(得分：0)
我编写将[1,0,0]，[0,1,0]，[0,0,1]更改为0,1,2的代码。
import numpy as np
import tensorflow as tf
def change_to_right(wrong_labels):
right_labels=[]
for x in wrong_labels:
for i in range(0,len(wrong_labels[0])):
if x[i]==1:
right_labels.append(i)
return right_labels
wrong_labels =np.array([[0,0,1,0], [0,0,1,0], [1,0,0,0],[0,1,0,0]])
right_labels =tf.convert_to_tensor(np.array(change_to_right(wrong_labels)))