I've been trying to create the convolutional network from the Tensorflow tutorial, but I've been having trouble. For some reason, I'm getting errors where the size of y_conv is 4x larger than the size of y_, and I have no idea why. I found this question, but it appears to be a different problem than mine, though it looks similar.
To be clear, the batch size in the below code is 50, but the error it's coming up with is
tensorflow.python.framework.errors.InvalidArgumentError: Incompatible shapes: [200] vs. [50]
and when I change the batch size to 10, I get
tensorflow.python.framework.errors.InvalidArgumentError: Incompatible shapes: [40] vs. [10]
so it's related to the batch size somehow, but I can't figure that out. Can anybody tell me what's wrong with this code? It's pretty much straight from the tutorial linked above.
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
import tensorflow as tf
sess = tf.InteractiveSession()
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides = [1, 2, 2, 1], padding='SAME')
x = tf.placeholder("float", shape=[None, 784])
y_ = tf.placeholder("float", shape=[None, 10])
w_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1, 28, 28, 1])
h_conv1 = tf.nn.relu(conv2d(x_image, w_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
w_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_conv1, w_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
w_fc1 = weight_variable([7*7*64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1)
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
cross_entropy = -tf.reduce_sum(y_ * tf.log(tf.clip_by_value(y_conv, 1e-10, 1.0)))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
sess.run(tf.initialize_all_variables())
for i in range(20000):
batch = mnist.train.next_batch(50)
if i%100 == 0:
train_accuracy = accuracy.eval(feed_dict={x:batch[0], y_: batch[1], keep_prob: 1.0})
print("step %d, training accuracy %g"%(i, train_accuracy))
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
print("test accuracy %g"%accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
The reshapes with the -1's are clues. It's not the batch size that's wrong it's the image size. You're flattening it out into the batch dimension.
Why is the image the wrong size?
On the second conv you're passing conv1 instead of pool1
conv2d(h_conv1, w_conv2).
Personally for pipelines like this I like to use 1 name for the data as it flows through.
Start using a debugger, it's worth it!
Related
I'm training a CNN model to recognize MNIST datasets, when i run this code, my IDE became unresponsive, i review many times but can't find where is wrong.Here is the code:
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
def weight(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding="SAME")
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
sess = tf.InteractiveSession()
W_conv1 = weight([5, 5, 1, 32])
b_conv1 = bias([32])
x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
x_image = tf.reshape(x, shape=[-1, 28, 28, 1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
W_conv2 = weight([5, 5, 32, 64])
b_conv2 = bias([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
W_fc1 = weight([7*7*64, 1024])
b_fc1 = bias([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight([1024, 10])
b_fc2 = bias([10])
y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_, 1), tf.argmax(y_conv, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
sess.run(tf.initialize_all_variables())
for i in range(20000):
batch = mnist.train.next_batch(50)
if i % 100 == 0:
train_accuracy = accuracy.eval({x: batch[0], y_: batch[1], keep_prob: 1.0})
print("step %d, training accuracy %g" % (i, train_accuracy))
train_step.run({x: batch[0], y_: batch[1], keep_prob: 0.5})
print("test accuracy %g" % accuracy.eval({x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
i can't copy the running information, my cpu worked 100%.
it can just print "step 100, 0.12",and then no thing happened.
I'm new to machine learning and tensorflow. I started by following the MNIST tutorial on the tensorflow site. I got the simple version to work, but when I was following along with the deep CNN, I found an error.
ValueError: Shape must be rank 4 but is rank 1 for 'Conv2D' (op:
'Conv2D') with input shapes: [?,28,28,1], [4].
The problem seems to lie in the line:
x_image = tf.reshape(x, [-1, 28, 28, 1])
Thanks for any help, I'm a bit lost here.
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNST_data/", one_hot=True)
import tensorflow as tf
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.matmul(x, W) + b
y_ = tf.placeholder(tf.float32, [None, 10])
#improvements
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
#layer 1
W_conv1 = ([5,5,1,32])
b_conv1 = ([32])
x_image = tf.reshape(x, [-1, 28, 28, 1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
#layer 2
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
#fully connected layer
W_fc1 = weight_variable([3136, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 3136])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
#dropout
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
#readout, similar to softmax
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
#optimization
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
#training
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
#evaluate
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
#the session
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(20000):
batch = mnist.train.next_batch(50)
if i%100==0:
training_accuracy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})
print("step: %i accuracy: %a" % (i, training_accuracy))
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
print("test accuracy: %s" % accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
Your error is in your 1st convolutional layer - your variables W_conv1 and b_conv1 are just lists (hence rank 1) since you have not used the weight_variable() and bias_variable() functions that you created!
Might be relevant.
This error at least misleading and confusing for me.
As per the error its asking us to check "input shapes", whereas exactly the issue is in filters that you have specified.
That's why #Yuji asking above to use method weight_variable(), which is properly initializing the filters(weights).
i am now trying to learn tensorflow so any assistance is appreciated. I followed the mnist code posted on the tensorflow website: https://www.tensorflow.org/get_started/mnist/pros
The model runs and trains to 99% plus accuracy. I downloaded a png image from the internet of a number one..lets call it 1.png. How do i now input this image into my trained model to determine if it recogonizes it as a one? None of the youtube videos i looked at so far or even the tensorflow page explains how to do this. What do i type to get this image to be checked by the model? There must be a way to pass in a single image to the model after it is trained otherwise there would be no point to reaching the stage of a trained model. The total code i use is below (which is the same code shown on the tensorflow website):
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
import tensorflow as tf
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
sess.run(tf.global_variables_initializer())
writer = tf.summary.FileWriter('/tmp/mnistworking', graph=sess.graph)
y = tf.matmul(x,W) + b
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
for _ in range(1000):
batch = mnist.train.next_batch(100)
train_step.run(feed_dict={x: batch[0], y_: batch[1]})
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1,28,28,1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
sess.run(tf.global_variables_initializer())
for i in range(17000):
batch = mnist.train.next_batch(50)
if i%100 == 0:
train_accuracy = accuracy.eval(feed_dict={
x:batch[0], y_: batch[1], keep_prob: 1.0})
print("step %d, training accuracy %g"%(i, train_accuracy))
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
print("test accuracy %g"%accuracy.eval(feed_dict={
x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
You need to do the following:
Restore the model from a saved checkpoint. There are several ways how this can be achieved.
Load your test image from disk into a numpy array, vectorize and reshape it to be of size [1, 784] because this is the shape of your input placeholder defined here: x = tf.placeholder(tf.float32, shape=[None, 784]). Note that None in this case stands for a variable batch size, so it is okay to just feed one data point at test time, as you intend to do.
Next you let the model do its work, i.e. let it predict. For this you need to fetch the node that computes the classification, which seems to be tf.argmax(y_conv, 1) in the code you posted. Note that you do not need to feed a label into the model, because you are not performing a training step during test time.
Also, may be this tutorial can be helpful for you: Tensorflow Mechanics 101
I've been following the tensorflow tutorials. I've imported the MNIST dataset and ran the code for a 2 layer convolutional neural net. It took nearly 45 minutes to train. I want to cut down the training data by discarding some of the data. How do I do that?
Here's the code:
import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')
x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1,28,28,1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv, y_))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
sess = tf.Session()
sess.run(tf.initialize_all_variables())
for i in range(20000):
batch = mnist.train.next_batch(50)
if i%100 == 0:
train_accuracy = accuracy.eval(session=sess,feed_dict={x:batch[0], y_: batch[1], keep_prob: 1.0})
print("step %d, training accuracy %g"%(i, train_accuracy))
train_step.run(session=sess,feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
print("test accuracy %g"%accuracy.eval(session=sess,feed_dict={x: np.split(mnist.test.images,5)[0], y_: np.split(mnist.test.labels,5)[0], keep_prob: 1.0}))
I cut down the size of testing data since it's a numpy array. How do I do the same for training data?
You are using dataset provider defined in https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/learn/python/learn/datasets/mnist.py
To reduce number of training sample, you can change this file (Line 237), or create a modified version and use it, instead of
from tensorflow.examples.tutorials.mnist import input_data
Which points to the link that I mentioned above.
Cutting your training sample will not help you in any good way - as far as you use minibatches, it doesn't affect performance directly. As a better alternative, you can reduce the number of epochs and/or increase the learning rate.
Reducing data sample is a very bad idea in this case
just one question - we are talking about this code `https://www.tensorflow.org/tutorials/mnist/pros/ ???
So if this needs 45 minutes, i guess you are running on cpu - you should think of using gpu. I tested the code in an Azure VM N Series with a Tesla K 80 GPU and it finishes in 4 Minutes
I'm trying to run a 2-layer convolutional net for digit recognition on MNIST data-set using Tensorflow in Python3.5. The input is from csv files which I have read in as pandas dataframe. Tensorflow doesn't like pandas dataframe (it didn't accept the input), so I changed it to a numpy array. The following is the entire code-
sess=tf.InteractiveSession()
train=pd.read_csv('train (1).csv',sep=',',header=0,dtype='float32')
x_train=train.iloc[:,1:]
y_train=train.iloc[:,0]
onehot=OneHotEncoder()
y_train=y_train.reshape(-1,1)
y_train=onehot.fit_transform(y_train)
test=pd.read_csv('test.csv',sep=',',header=0)
x=tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
W=tf.Variable(tf.zeros([784,10]))
b=tf.Variable(tf.zeros([10]))
y=tf.nn.softmax(tf.matmul(x,W) +b )
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
x_train= x_train.as_matrix()
x_image = tf.reshape(x_train, [-1,28,28,1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv, y_))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
sess.run(tf.initialize_all_variables())
k=0
for i in range(20000):
x_batch = x_train[k*100:k+100,:]
y_batch = y_train[k*100:k+100,:]
if i%100 == 0:
train_accuracy = accuracy.eval(feed_dict={
x:x_batch, y_: y_batch, keep_prob: 1.0})
print("step %d, training accuracy %g"%(i, train_accuracy))
train_step.run(feed_dict={x: x_batch, y_: y_batch, keep_prob: 0.5})
k+=1
The error which I'm getting is in the accuracy.eval function-
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
The following is the error message-
ValueError: setting an array element with a sequence.
I tried looking up why the error occurs, and there were several reasons for it. However, my input is in the form of an array and I'm not really familiar with tensors, so I'm trouble understanding what is going wrong.
Any help is appreciated.