InvalidArgumentError for placeholder
0
$begingroup$
I'm copying an example directly out of a book I am working through, and I currently getting this error:
InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder_10' with dtype float and shape [?,2]
[[{{node Placeholder_10}} = Placeholder[dtype=DT_FLOAT, shape=[?,2], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
My tensors and placeholders are declared like so:
XOR_X = [[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]
XOR_Y = [[0.0], [1.0], [1.0], [0.0]]
num_input = 2
num_classes = 1
x_ = tf.placeholder("float", shape=[None, num_input], name='X')
y_ = tf.placeholder("float", shape=[None, num_classes], name='Y')
And the line producing the error is here:
_, summary = sess.run([train_step, merged_summary_op], feed_dict={x_ : XOR_X, y_: XOR_Y})
What exactly is the issue here?
for context, I attach the entire implementation here:
import tensorflow as tf
XOR_X = [[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]
XOR_Y = [[0], [1], [1], [0]]
# XOR_Y = [0.0, 1.0, 1.0, 0.0]
num_input = 2
num_classes = 1
x_ = tf.placeholder("float", shape=[None, num_input], name='X')
y_ = tf.placeholder("float", shape=[None, num_classes], name='Y')
#Model structure
H1 = tf.layers.dense(inputs=x_, units=4, activation=tf.nn.sigmoid)
H2 = tf.layers.dense(inputs=H1, units=8, activation=tf.nn.sigmoid)
H_OUT = tf.layers.dense(inputs=H2, units=num_classes, activation=tf.nn.sigmoid)
#Define cost function
with tf.name_scope("cost") as scope:
cost = tf.losses.log_loss( labels=y_, predictions=H_OUT)
# Add loss to tensorboard
tf.summary.scalar("log_loss", cost)
with tf.name_scope("train") as scope:
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(cost)
merged_summary_op = tf.summary.merge_all()
# Initialize variables(weights) and session
init = tf.global_variables_initializer()
sess = tf.Session()
# Configure summary to output at given directory
writer = tf.summary.FileWriter("./logs/xor_logs", sess.graph)
sess.run(init)
# Train loop
for step in range(10000):
# Run train_step and merge summary op
_, summary = sess.run([train_step, merged_summary_op], feed_dict={x_ : XOR_X, y_: XOR_Y})
if step % 1000 == 0:
print("Step/Epoch: {}, Loss: {}".format(step, sess.run(cost, feed_dict={x_ : XOR_X, y_: XOR_Y})))
writer.add_summary(summary, step)
tensorflow
asked 3 hours ago
saksak
101
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0
$begingroup$
I'm copying an example directly out of a book I am working through, and I currently getting this error:
InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder_10' with dtype float and shape [?,2]
[[{{node Placeholder_10}} = Placeholder[dtype=DT_FLOAT, shape=[?,2], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
My tensors and placeholders are declared like so:
XOR_X = [[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]
XOR_Y = [[0.0], [1.0], [1.0], [0.0]]
num_input = 2
num_classes = 1
x_ = tf.placeholder("float", shape=[None, num_input], name='X')
y_ = tf.placeholder("float", shape=[None, num_classes], name='Y')
And the line producing the error is here:
_, summary = sess.run([train_step, merged_summary_op], feed_dict={x_ : XOR_X, y_: XOR_Y})
What exactly is the issue here?
for context, I attach the entire implementation here:
import tensorflow as tf
XOR_X = [[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]
XOR_Y = [[0], [1], [1], [0]]
# XOR_Y = [0.0, 1.0, 1.0, 0.0]
num_input = 2
num_classes = 1
x_ = tf.placeholder("float", shape=[None, num_input], name='X')
y_ = tf.placeholder("float", shape=[None, num_classes], name='Y')
#Model structure
H1 = tf.layers.dense(inputs=x_, units=4, activation=tf.nn.sigmoid)
H2 = tf.layers.dense(inputs=H1, units=8, activation=tf.nn.sigmoid)
H_OUT = tf.layers.dense(inputs=H2, units=num_classes, activation=tf.nn.sigmoid)
#Define cost function
with tf.name_scope("cost") as scope:
cost = tf.losses.log_loss( labels=y_, predictions=H_OUT)
# Add loss to tensorboard
tf.summary.scalar("log_loss", cost)
with tf.name_scope("train") as scope:
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(cost)
merged_summary_op = tf.summary.merge_all()
# Initialize variables(weights) and session
init = tf.global_variables_initializer()
sess = tf.Session()
# Configure summary to output at given directory
writer = tf.summary.FileWriter("./logs/xor_logs", sess.graph)
sess.run(init)
# Train loop
for step in range(10000):
# Run train_step and merge summary op
_, summary = sess.run([train_step, merged_summary_op], feed_dict={x_ : XOR_X, y_: XOR_Y})
if step % 1000 == 0:
print("Step/Epoch: {}, Loss: {}".format(step, sess.run(cost, feed_dict={x_ : XOR_X, y_: XOR_Y})))
writer.add_summary(summary, step)
tensorflow
asked 3 hours ago
saksak
101
New contributor
sak is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
0
0
0
$begingroup$
I'm copying an example directly out of a book I am working through, and I currently getting this error:
InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder_10' with dtype float and shape [?,2]
[[{{node Placeholder_10}} = Placeholder[dtype=DT_FLOAT, shape=[?,2], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
My tensors and placeholders are declared like so:
XOR_X = [[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]
XOR_Y = [[0.0], [1.0], [1.0], [0.0]]
num_input = 2
num_classes = 1
x_ = tf.placeholder("float", shape=[None, num_input], name='X')
y_ = tf.placeholder("float", shape=[None, num_classes], name='Y')
And the line producing the error is here:
_, summary = sess.run([train_step, merged_summary_op], feed_dict={x_ : XOR_X, y_: XOR_Y})
What exactly is the issue here?
for context, I attach the entire implementation here:
import tensorflow as tf
XOR_X = [[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]
XOR_Y = [[0], [1], [1], [0]]
# XOR_Y = [0.0, 1.0, 1.0, 0.0]
num_input = 2
num_classes = 1
x_ = tf.placeholder("float", shape=[None, num_input], name='X')
y_ = tf.placeholder("float", shape=[None, num_classes], name='Y')
#Model structure
H1 = tf.layers.dense(inputs=x_, units=4, activation=tf.nn.sigmoid)
H2 = tf.layers.dense(inputs=H1, units=8, activation=tf.nn.sigmoid)
H_OUT = tf.layers.dense(inputs=H2, units=num_classes, activation=tf.nn.sigmoid)
#Define cost function
with tf.name_scope("cost") as scope:
cost = tf.losses.log_loss( labels=y_, predictions=H_OUT)
# Add loss to tensorboard
tf.summary.scalar("log_loss", cost)
with tf.name_scope("train") as scope:
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(cost)
merged_summary_op = tf.summary.merge_all()
# Initialize variables(weights) and session
init = tf.global_variables_initializer()
sess = tf.Session()
# Configure summary to output at given directory
writer = tf.summary.FileWriter("./logs/xor_logs", sess.graph)
sess.run(init)
# Train loop
for step in range(10000):
# Run train_step and merge summary op
_, summary = sess.run([train_step, merged_summary_op], feed_dict={x_ : XOR_X, y_: XOR_Y})
if step % 1000 == 0:
print("Step/Epoch: {}, Loss: {}".format(step, sess.run(cost, feed_dict={x_ : XOR_X, y_: XOR_Y})))
writer.add_summary(summary, step)
tensorflow
asked 3 hours ago
saksak
101
New contributor
sak is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I'm copying an example directly out of a book I am working through, and I currently getting this error:
InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder_10' with dtype float and shape [?,2]
[[{{node Placeholder_10}} = Placeholder[dtype=DT_FLOAT, shape=[?,2], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
My tensors and placeholders are declared like so:
XOR_X = [[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]
XOR_Y = [[0.0], [1.0], [1.0], [0.0]]
num_input = 2
num_classes = 1
x_ = tf.placeholder("float", shape=[None, num_input], name='X')
y_ = tf.placeholder("float", shape=[None, num_classes], name='Y')
And the line producing the error is here:
_, summary = sess.run([train_step, merged_summary_op], feed_dict={x_ : XOR_X, y_: XOR_Y})
What exactly is the issue here?
for context, I attach the entire implementation here:
import tensorflow as tf
XOR_X = [[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]
XOR_Y = [[0], [1], [1], [0]]
# XOR_Y = [0.0, 1.0, 1.0, 0.0]
num_input = 2
num_classes = 1
x_ = tf.placeholder("float", shape=[None, num_input], name='X')
y_ = tf.placeholder("float", shape=[None, num_classes], name='Y')
#Model structure
H1 = tf.layers.dense(inputs=x_, units=4, activation=tf.nn.sigmoid)
H2 = tf.layers.dense(inputs=H1, units=8, activation=tf.nn.sigmoid)
H_OUT = tf.layers.dense(inputs=H2, units=num_classes, activation=tf.nn.sigmoid)
#Define cost function
with tf.name_scope("cost") as scope:
cost = tf.losses.log_loss( labels=y_, predictions=H_OUT)
# Add loss to tensorboard
tf.summary.scalar("log_loss", cost)
with tf.name_scope("train") as scope:
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(cost)
merged_summary_op = tf.summary.merge_all()
# Initialize variables(weights) and session
init = tf.global_variables_initializer()
sess = tf.Session()
# Configure summary to output at given directory
writer = tf.summary.FileWriter("./logs/xor_logs", sess.graph)
sess.run(init)
# Train loop
for step in range(10000):
# Run train_step and merge summary op
_, summary = sess.run([train_step, merged_summary_op], feed_dict={x_ : XOR_X, y_: XOR_Y})
if step % 1000 == 0:
print("Step/Epoch: {}, Loss: {}".format(step, sess.run(cost, feed_dict={x_ : XOR_X, y_: XOR_Y})))
writer.add_summary(summary, step)
tensorflow
tensorflow
asked 3 hours ago
saksak
101
New contributor
sak is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 hours ago
saksak
101
New contributor
sak is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 hours ago
saksak
101
New contributor
sak is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 hours ago
saksak
101
asked 3 hours ago
saksak
101
101
New contributor
sak is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
sak is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
sak is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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