ValueError: Cannot feed value of shape (2, 4) for Tensor u'InputData/X:0', which has shape '(?, 2, 4
ValueError: Cannot feed value of shape (2, 4) for Tensor u'InputData/X:0', which has shape '(?, 2, 4
我收到一个错误，ValueError：无法为形状为“(?, 2, 4, 104)”的 Tensor u'InputData/X:0' 提供形状 (2, 4) 的值。
我写代码，
# coding: utf-8
import tensorflow as tf
import tflearn
from tflearn.layers.core import input_data,dropout,fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.normalization import local_response_normalization
from tflearn.layers.estimator import regression
import pandas as pd
import numpy as np
from sklearn import metrics
tf.reset_default_graph()
net = input_data(shape=[2, 4, 104])
net = conv_2d(net, 4, 16, activation='relu')
net = max_pool_2d(net, 1)
net = tflearn.activations.relu(net)
net = dropout(net, 0.5)
net = tflearn.fully_connected(net, 10, activation='softmax')
net = tflearn.regression(net, optimizer='adam', learning_rate=0.5, loss='categorical_crossentropy')
model = tflearn.DNN(net)
trainDataSet = [[0.25,0.25,1,1],[0,0,1,1],[0.25,0.25,1,1]]
trainLabel = [[0,1],[0,1],[1,0]]
model.fit(trainDataSet, trainLabel, n_epoch=100, batch_size=32, validation_set=0.1, show_metric=True)
追溯说
Traceback (most recent call last):
File "cnn.py", line 16, in <module>
model.fit(trainDataSet, trainLabel, n_epoch=100, batch_size=32, validation_set=0.1, show_metric=True)
File "/Users/xxx/anaconda/xxx/lib/python2.7/site-packages/tflearn/models/dnn.py", line 216, in fit
callbacks=callbacks)
File "/Users/xxx/anaconda/xxx/lib/python2.7/site-packages/tflearn/helpers/trainer.py", line 339, in fit
show_metric)
File "/Users/xxx/anaconda/xxx/lib/python2.7/site-packages/tflearn/helpers/trainer.py", line 818, in _train
feed_batch)
File "/Users/xxx/anaconda/xxx/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 929, in run
run_metadata_ptr)
File "/Users/xxx/anaconda/xxx/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1128, in _run
str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (2, 4) for Tensor u'InputData/X:0', which has shape '(?, 2, 4, 104)'
我重写成
trainDataSet = np.array([[0.25,0.25,1,1],[0,0,1,1],[0.25,0.25,1,1]])
trainLabel = np.array([[0,1],[0,1],[1,0]])
但同样的错误 happens.What 在我的代码中是错误的？我应该如何解决这个问题？
引用自 Tensorflow 文档：
tflearn.layers.conv.conv_2d
Input:
4-D Tensor [batch, height, width, in_channels].
来自其他 Tensorflow 文档：
tf.nn.conv2d
Computes a 2-D convolution given 4-D input and filter tensors.
Given an input tensor of shape [batch, in_height, in_width,
in_channels] and a filter / kernel tensor of shape [filter_height,
filter_width, in_channels, out_channels], this op performs the
following:
您的数据集、标签和输入形状未对齐，即彼此不匹配。
目前你的 trainDataSet 的形状为 (3,4):
import numpy as np
trainDataSet = np.array([[0.25,0.25,1,1],[0,0,1,1],[0.25,0.25,1,1]])
print(trainDataSet.shape)
输出：
(3, 4)
但是您将输入形状定义为：
net = input_data(shape=[2, 4, 104])
你真正想要实现的目标不明确，但如果你想看到一个简单的工作示例，你的代码应该如下所示：
import tensorflow as tf
import tflearn
from tflearn.layers.core import input_data,dropout,fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.normalization import local_response_normalization
from tflearn.layers.estimator import regression
import pandas as pd
import numpy as np
from sklearn import metrics
tf.reset_default_graph()
net = input_data(shape=[3, 4, 1])
net = conv_2d(net, 4, 16, activation='relu')
net = max_pool_2d(net, 1)
net = tflearn.activations.relu(net)
net = dropout(net, 0.5)
net = tflearn.fully_connected(net, 2, activation='softmax')
net = tflearn.regression(net, optimizer='adam', learning_rate=0.5, loss='categorical_crossentropy')
model = tflearn.DNN(net)
trainDataSet = [
[
[[0.25], [0.25], [1], [1]],
[[0], [0], [1], [1]],
[[0.25], [0.25], [1], [1]]
],
[
[[0.25], [0.25], [1], [1]],
[[0], [0], [1], [1]],
[[0.25], [0.25], [1], [1]]
],
[
[[0.25], [0.25], [1], [1]],
[[0], [0], [1], [1]],
[[0.25], [0.25], [1], [1]]
]
]
trainLabel = [[0,1],[0,1],[1,0]]
model.fit(trainDataSet, trainLabel, n_epoch=100, batch_size=32, validation_set=0.1, show_metric=True)
输出：
---------------------------------
Run id: NHHJV7
Log directory: /tmp/tflearn_logs/
INFO:tensorflow:Summary name Accuracy/ (raw) is illegal; using Accuracy/__raw_ instead.
---------------------------------
Training samples: 2
Validation samples: 1
--
Training Step: 1 | time: 1.160s
| Adam | epoch: 001 | loss: 0.00000 - acc: 0.0000 | val_loss: 23.02585 - val_acc: 0.0000 -- iter: 2/2
--
Training Step: 2 | total loss: 0.62966 | time: 1.008s
| Adam | epoch: 002 | loss: 0.62966 - acc: 0.0000 | val_loss: 10.76885 - val_acc: 0.0000 -- iter: 2/2
.
.
.
Training Step: 99 | total loss: 0.00000 | time: 1.013s
| Adam | epoch: 099 | loss: 0.00000 - acc: 1.0000 | val_loss: 23.02585 - val_acc: 0.0000 -- iter: 2/2
--
Training Step: 100 | total loss: 0.00000 | time: 1.011s
| Adam | epoch: 100 | loss: 0.00000 - acc: 1.0000 | val_loss: 23.02585 - val_acc: 0.0000 -- iter: 2/2
--
我收到一个错误，ValueError：无法为形状为“(?, 2, 4, 104)”的 Tensor u'InputData/X:0' 提供形状 (2, 4) 的值。 我写代码，
# coding: utf-8
import tensorflow as tf
import tflearn
from tflearn.layers.core import input_data,dropout,fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.normalization import local_response_normalization
from tflearn.layers.estimator import regression
import pandas as pd
import numpy as np
from sklearn import metrics
tf.reset_default_graph()
net = input_data(shape=[2, 4, 104])
net = conv_2d(net, 4, 16, activation='relu')
net = max_pool_2d(net, 1)
net = tflearn.activations.relu(net)
net = dropout(net, 0.5)
net = tflearn.fully_connected(net, 10, activation='softmax')
net = tflearn.regression(net, optimizer='adam', learning_rate=0.5, loss='categorical_crossentropy')
model = tflearn.DNN(net)
trainDataSet = [[0.25,0.25,1,1],[0,0,1,1],[0.25,0.25,1,1]]
trainLabel = [[0,1],[0,1],[1,0]]
model.fit(trainDataSet, trainLabel, n_epoch=100, batch_size=32, validation_set=0.1, show_metric=True)
追溯说
Traceback (most recent call last):
File "cnn.py", line 16, in <module>
model.fit(trainDataSet, trainLabel, n_epoch=100, batch_size=32, validation_set=0.1, show_metric=True)
File "/Users/xxx/anaconda/xxx/lib/python2.7/site-packages/tflearn/models/dnn.py", line 216, in fit
callbacks=callbacks)
File "/Users/xxx/anaconda/xxx/lib/python2.7/site-packages/tflearn/helpers/trainer.py", line 339, in fit
show_metric)
File "/Users/xxx/anaconda/xxx/lib/python2.7/site-packages/tflearn/helpers/trainer.py", line 818, in _train
feed_batch)
File "/Users/xxx/anaconda/xxx/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 929, in run
run_metadata_ptr)
File "/Users/xxx/anaconda/xxx/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1128, in _run
str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (2, 4) for Tensor u'InputData/X:0', which has shape '(?, 2, 4, 104)'
我重写成
trainDataSet = np.array([[0.25,0.25,1,1],[0,0,1,1],[0.25,0.25,1,1]])
trainLabel = np.array([[0,1],[0,1],[1,0]])
但同样的错误 happens.What 在我的代码中是错误的？我应该如何解决这个问题？
引用自 Tensorflow 文档：
tflearn.layers.conv.conv_2d
Input:
4-D Tensor [batch, height, width, in_channels].
来自其他 Tensorflow 文档：
tf.nn.conv2d
Computes a 2-D convolution given 4-D input and filter tensors.
Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:
您的数据集、标签和输入形状未对齐，即彼此不匹配。
目前你的 trainDataSet 的形状为 (3,4):
import numpy as np
trainDataSet = np.array([[0.25,0.25,1,1],[0,0,1,1],[0.25,0.25,1,1]])
print(trainDataSet.shape)
输出：
(3, 4)
但是您将输入形状定义为：
net = input_data(shape=[2, 4, 104])
你真正想要实现的目标不明确，但如果你想看到一个简单的工作示例，你的代码应该如下所示：
import tensorflow as tf
import tflearn
from tflearn.layers.core import input_data,dropout,fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.normalization import local_response_normalization
from tflearn.layers.estimator import regression
import pandas as pd
import numpy as np
from sklearn import metrics
tf.reset_default_graph()
net = input_data(shape=[3, 4, 1])
net = conv_2d(net, 4, 16, activation='relu')
net = max_pool_2d(net, 1)
net = tflearn.activations.relu(net)
net = dropout(net, 0.5)
net = tflearn.fully_connected(net, 2, activation='softmax')
net = tflearn.regression(net, optimizer='adam', learning_rate=0.5, loss='categorical_crossentropy')
model = tflearn.DNN(net)
trainDataSet = [
[
[[0.25], [0.25], [1], [1]],
[[0], [0], [1], [1]],
[[0.25], [0.25], [1], [1]]
],
[
[[0.25], [0.25], [1], [1]],
[[0], [0], [1], [1]],
[[0.25], [0.25], [1], [1]]
],
[
[[0.25], [0.25], [1], [1]],
[[0], [0], [1], [1]],
[[0.25], [0.25], [1], [1]]
]
]
trainLabel = [[0,1],[0,1],[1,0]]
model.fit(trainDataSet, trainLabel, n_epoch=100, batch_size=32, validation_set=0.1, show_metric=True)
输出：
---------------------------------
Run id: NHHJV7
Log directory: /tmp/tflearn_logs/
INFO:tensorflow:Summary name Accuracy/ (raw) is illegal; using Accuracy/__raw_ instead.
---------------------------------
Training samples: 2
Validation samples: 1
--
Training Step: 1 | time: 1.160s
| Adam | epoch: 001 | loss: 0.00000 - acc: 0.0000 | val_loss: 23.02585 - val_acc: 0.0000 -- iter: 2/2
--
Training Step: 2 | total loss: 0.62966 | time: 1.008s
| Adam | epoch: 002 | loss: 0.62966 - acc: 0.0000 | val_loss: 10.76885 - val_acc: 0.0000 -- iter: 2/2
.
.
.
Training Step: 99 | total loss: 0.00000 | time: 1.013s
| Adam | epoch: 099 | loss: 0.00000 - acc: 1.0000 | val_loss: 23.02585 - val_acc: 0.0000 -- iter: 2/2
--
Training Step: 100 | total loss: 0.00000 | time: 1.011s
| Adam | epoch: 100 | loss: 0.00000 - acc: 1.0000 | val_loss: 23.02585 - val_acc: 0.0000 -- iter: 2/2
--