Kendall Weihe Kendall Weihe - 5 months ago 116
Python Question

Tensorflow softmax_cross...() function float type error

I resolved in issue from this post, and the use Olivier recommended using the

softmax_cross_entropy_with_logits()
function. He is correct, but I'm getting a weird data type error.

The error is

tensorflow.python.framework.errors.InvalidArgumentError: You must feed a value for placeholder tensor 'ground_truth' with dtype float
[[Node: ground_truth = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]


The softmax loss function requires data type float... which I have (it even says
ground_truth = Placeholder[dtype=DT_FLOAT
in the error message.

Here is my definition:
y = tf.placeholder(tf.float32, [None, n_input, n_input, n_classes], name="ground_truth")


then I reshape and input into the loss function

temp_y = tf.reshape(y, [-1, 2])
cost = (tf.nn.softmax_cross_entropy_with_logits(temp_pred, temp_y))


the error happens when I run a loss and accuracy session

loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,
temp_y: batch_y,
keep_prob: 1.0})


but debugging right above that step I have

(Pdb) cost
<tf.Tensor 'loss/SoftmaxCrossEntropyWithLogits:0' shape=(?,) dtype=float32>
(Pdb) accuracy
<tf.Tensor 'acc/Mean:0' shape=() dtype=float32>
(Pdb) x
<tf.Tensor 'Placeholder:0' shape=(?, 200, 200) dtype=float32>
(Pdb) batch_x.dtype
dtype('float64')
(Pdb) temp_y
<tf.Tensor 'loss/Reshape_1:0' shape=(?, 2) dtype=float32>
(Pdb) batch_y.dtype
dtype('float64')
(Pdb) batch_x.shape
(10, 200, 200)
(Pdb) batch_y.shape
(400000, 2)


So you can see they are all of type float, so why is it throwing a data type error?

I've also tried:
batch_y = np.float32(batch_y)
but no luck

The full code is here:

import tensorflow as tf
import pdb
import numpy as np
from numpy import genfromtxt
from PIL import Image

# Parameters
learning_rate = 0.001
training_iters = 10000
batch_size = 10
display_step = 1

# Network Parameters
n_input = 200 # MNIST data input (img shape: 28*28)
n_output = 40000
n_classes = 2 # MNIST total classes (0-9 digits)
#n_input = 200

dropout = 0.75 # Dropout, probability to keep units

# tf Graph input
x = tf.placeholder(tf.float32, [None, n_input, n_input])
y = tf.placeholder(tf.float32, [None, n_input, n_input, n_classes], name="ground_truth")
keep_prob = tf.placeholder(tf.float32) #dropout (keep probability)


def convert_to_2_channel(x, batch_size):
#assume input has dimension (batch_size,x,y)
#output will have dimension (batch_size,x,y,2)
output = np.empty((batch_size, 200, 200, 2))

temp_arr1 = np.empty((batch_size, 200, 200))
temp_arr2 = np.empty((batch_size, 200, 200))

for i in xrange(batch_size):
for j in xrange(200):
for k in xrange(200):
if x[i][j][k] == 1:
temp_arr1[i][j][k] = 1
temp_arr2[i][j][k] = 0
else:
temp_arr1[i][j][k] = 0
temp_arr2[i][j][k] = 1

for i in xrange(batch_size):
for j in xrange(200):
for k in xrange(200):
for l in xrange(2):
if l == 0:
output[i][j][k][l] = temp_arr1[i][j][k]
else:
output[i][j][k][l] = temp_arr2[i][j][k]

return output


# Create some wrappers for simplicity
def conv2d(x, W, b, strides=1):
# Conv2D wrapper, with bias and relu activation
x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
x = tf.nn.bias_add(x, b)
return tf.nn.relu(x)

def maxpool2d(x, k=2):
# MaxPool2D wrapper
return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],
padding='SAME')


# Create model
def conv_net(x, weights, biases, dropout):
# Reshape input picture
x = tf.reshape(x, shape=[-1, 200, 200, 1])

with tf.name_scope("conv1") as scope:
# Convolution Layer
conv1 = conv2d(x, weights['wc1'], biases['bc1'])
# Max Pooling (down-sampling)
#conv1 = tf.nn.local_response_normalization(conv1)
conv1 = maxpool2d(conv1, k=2)

# Convolution Layer
with tf.name_scope("conv2") as scope:
conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
# Max Pooling (down-sampling)
#conv2 = tf.nn.local_response_normalization(conv2)
conv2 = maxpool2d(conv2, k=2)

# Convolution Layer
with tf.name_scope("conv3") as scope:
conv3 = conv2d(conv2, weights['wc3'], biases['bc3'])
# # Max Pooling (down-sampling)
#conv3 = tf.nn.local_response_normalization(conv3)
conv3 = maxpool2d(conv3, k=2)


temp_batch_size = tf.shape(x)[0]
with tf.name_scope("deconv1") as scope:
output_shape = [temp_batch_size, 50, 50, 64]
conv4 = tf.nn.conv2d_transpose(conv3, weights['wdc1'], output_shape=output_shape, strides=[1,2,2,1], padding="VALID")
conv4 = tf.nn.bias_add(conv4, biases['bdc1'])
conv4 = tf.nn.relu(conv4)
# conv4 = tf.nn.local_response_normalization(conv4)

with tf.name_scope("deconv2") as scope:
# output_shape = tf.pack([temp_batch_size, 100, 100, 32])
output_shape = [temp_batch_size, 100, 100, 32]
conv5 = tf.nn.conv2d_transpose(conv4, weights['wdc2'], output_shape=output_shape, strides=[1,2,2,1], padding="VALID")
conv5 = tf.nn.bias_add(conv5, biases['bdc2'])
conv5 = tf.nn.relu(conv5)
# conv5 = tf.nn.local_response_normalization(conv5)

with tf.name_scope("deconv3") as scope:
# output_shape = tf.pack([temp_batch_size, 200, 200, 1])
output_shape = [temp_batch_size, 200, 200, 2]
conv6 = tf.nn.conv2d_transpose(conv5, weights['wdc3'], output_shape=output_shape, strides=[1,2,2,1], padding="VALID")
conv6 = tf.nn.bias_add(conv6, biases['bdc3'])
# conv6 = tf.nn.relu(conv6)
# pdb.set_trace()
conv6 = tf.nn.dropout(conv6, dropout)

return conv6
# Fully connected layer
# Reshape conv2 output to fit fully connected layer input
# fc1 = tf.reshape(conv6, [-1, weights['wd1'].get_shape().as_list()[0]])
# fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
# fc1 = tf.nn.relu(fc1)
# # Apply Dropout
# fc1 = tf.nn.dropout(fc1, dropout)
#
# return (tf.add(tf.matmul(fc1, weights['out']), biases['out']))# Store layers weight & bias

weights = {
# 5x5 conv, 1 input, 32 outputs
'wc1' : tf.Variable(tf.random_normal([5, 5, 1, 32])),
# 5x5 conv, 32 inputs, 64 outputs
'wc2' : tf.Variable(tf.random_normal([5, 5, 32, 64])),
# 5x5 conv, 32 inputs, 64 outputs
'wc3' : tf.Variable(tf.random_normal([5, 5, 64, 128])),

'wdc1' : tf.Variable(tf.random_normal([2, 2, 64, 128])),

'wdc2' : tf.Variable(tf.random_normal([2, 2, 32, 64])),

'wdc3' : tf.Variable(tf.random_normal([2, 2, 2, 32])),

# fully connected, 7*7*64 inputs, 1024 outputs
'wd1': tf.Variable(tf.random_normal([80000, 1024])),
# 1024 inputs, 10 outputs (class prediction)
'out': tf.Variable(tf.random_normal([1024, 80000]))
}

biases = {
'bc1': tf.Variable(tf.random_normal([32])),
'bc2': tf.Variable(tf.random_normal([64])),
'bc3': tf.Variable(tf.random_normal([128])),
'bdc1': tf.Variable(tf.random_normal([64])),
'bdc2': tf.Variable(tf.random_normal([32])),
'bdc3': tf.Variable(tf.random_normal([2])),
'bd1': tf.Variable(tf.random_normal([1024])),
'out': tf.Variable(tf.random_normal([80000]))
}

# Construct model
# with tf.name_scope("net") as scope:
pred = conv_net(x, weights, biases, keep_prob)
pred = tf.reshape(pred, [-1,n_input,n_input,n_classes])
# Define loss and optimizer
with tf.name_scope("loss") as scope:
# cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(pred, y))
temp_pred = tf.reshape(pred, [-1, 2])
temp_y = tf.reshape(y, [-1, 2])
cost = (tf.nn.softmax_cross_entropy_with_logits(temp_pred, temp_y))

with tf.name_scope("opt") as scope:
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
# optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)


# Evaluate model
with tf.name_scope("acc") as scope:
correct_pred = tf.equal(0,tf.cast(tf.sub(tf.nn.softmax(temp_pred),y), tf.int32))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# Initializing the variables
init = tf.initialize_all_variables()
saver = tf.train.Saver()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
summary = tf.train.SummaryWriter('/tmp/logdir/', sess.graph)
step = 1
from tensorflow.contrib.learn.python.learn.datasets.scroll import scroll_data
data = scroll_data.read_data('/home/kendall/Desktop/')
# Keep training until reach max iterations
while step * batch_size < training_iters:
batch_x, batch_y = data.train.next_batch(batch_size)
# Run optimization op (backprop)
batch_x = batch_x.reshape((batch_size, n_input, n_input))
batch_y = batch_y.reshape((batch_size, n_input, n_input))
batch_y = convert_to_2_channel(batch_y, batch_size) #converts the 200x200 ground truth to a 200x200x2 classification
batch_y = batch_y.reshape(batch_size * n_input * n_input, 2)
sess.run(optimizer, feed_dict={x: batch_x, temp_y: batch_y,
keep_prob: dropout})
#measure prediction
prediction = sess.run(tf.nn.softmax(temp_pred), feed_dict={x: batch_x, keep_prob: 1.0})
print prediction
if step % display_step == 0:
# Calculate batch loss and accuracdef conv_net(x, weights, biases, dropout):
# save_path = "model.ckpt"
# saver.save(sess, save_path)
pdb.set_trace()
loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,
temp_y: batch_y,
keep_prob: 1.0})
print "Accuracy = " + str(acc)
if acc > 0.73:
break
step += 1
print "Optimization Finished!"

#make prediction
im = Image.open('/home/kendall/Desktop/HA900_frames/frame0035.tif')
batch_x = np.array(im)
# pdb.set_trace()
batch_x = batch_x.reshape((1, n_input, n_input))
batch_x = batch_x.astype(float)
pdb.set_trace()
prediction = sess.run(tf.nn.sigmoid(pred), feed_dict={x: batch_x, keep_prob: dropout})
print prediction
arr1 = np.empty((n_input,n_input))
arr2 = np.empty((n_input,n_input))
for i in xrange(n_input):
for j in xrange(n_input):
for k in xrange(2):
if k == 0:
arr1[i][j] = (prediction[0][i][j][k])
else:
arr2[i][j] = (prediction[0][i][j][k])
# prediction = np.asarray(prediction)
# prediction = np.reshape(prediction, (200,200))
# np.savetxt("prediction.csv", prediction, delimiter=",")
np.savetxt("prediction1.csv", arr1, delimiter=",")
np.savetxt("prediction2.csv", arr2, delimiter=",")
# np.savetxt("prediction2.csv", arr2, delimiter=",")

# Calculate accuracy for 256 mnist test images
print "Testing Accuracy:", \
sess.run(accuracy, feed_dict={x: data.test.images[:256],
y: data.test.labels[:256],
keep_prob: 1.})


EDIT I tried Olivier's suggestion below -- I have trained on a single image and made a prediction on the same image, however the results are noise (image below)

enter image description here

Answer

You have created a placeholder with:

y = tf.placeholder(tf.float32, [None, n_input, n_input, n_classes], name="ground_truth")

The error is quite clear:

You must feed a value for placeholder tensor 'ground_truth'

When calling sess.run([cost, accuracy], feed_dict={x: batch_x, temp_y: batch_y, keep_prob: 1.0}), you are not feeding the parameter y.

You should instead use:

batch_y = convert_to_2_channel(batch_y, batch_size)
# do not reshape batch_y now
sess.run([cost, accuracy], feed_dict={x: batch_x,
                                      y: batch_y,
                                      keep_prob: 1.0})