Tensorflow - You must feed a value for placeholder tensor 'X' with dtype float

Question

Please refer to below tensorflow code:

#!/usr/bin/env python3
import tensorflow as tf
import numpy as np
from sklearn.datasets import fetch_california_housing
import numpy.random as rnd

housing = fetch_california_housing()
m, n = (50000, 3)

n_epochs = 50000
learning_rate = 0.1

X = tf.placeholder(tf.float32, shape=(None, n + 1), name="X")
y = tf.placeholder(tf.float32, shape=(None, 1), name="y")
theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name="theta")
y_pred = tf.matmul(X, theta, name="predictions")
error = y_pred - y
mse = tf.reduce_mean(tf.square(error), name="mse")
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
training_op = optimizer.minimize(mse)

init = tf.global_variables_initializer()

ans_theta = np.array([[4],[3],[2],[1]]).astype(np.float32)
X_train = rnd.rand(m, n + 1)
y_train = X_train.dot(ans_theta).reshape(m, 1)
print("ans_theta=%s" % (ans_theta.transpose()))
print("X_train=%s" % (X_train[0]))
print("Expect y=%s"  % (np.sum(X_train[0] * ans_theta.transpose())))
print("y_train=%s" % (y_train[0]))
def fetch_batch(epoch, batch_index, batch_size):
    rnd.seed(epoch * n_batches + batch_index)
    indices = rnd.randint(m, size=batch_size)
    X_batch = X_train[indices]
    y_batch = y_train[indices]
    return X_batch, y_batch

n_epochs = 500
batch_size = 2000
n_batches = int(np.ceil(m / batch_size))

with tf.Session() as sess:
    sess.run(init)

    for epoch in range(n_epochs):
    #    for batch_index in range(n_batches):
    #        X_batch, y_batch = fetch_batch(epoch, batch_index, batch_size)
            #print("X_batch(%s):\n%s\n" % (X_batch.shape, X_batch[:1]))
            #print("y_batch(%s):\n%s\n" % (y_batch.shape, y_batch[:1]))
    #        sess.run(training_op, feed_dict={X: X_batch, y: y_batch})
        sess.run(training_op, feed_dict={X:X_train, y:y_train})
    best_theta = theta.eval()
    print("MSE=%s" % (mse.eval()))
print("Best theta:")
print(best_theta)

It will cause exception as below:

Caused by op 'X', defined at: File "./ch9_t00.py", line 19, in X = tf.placeholder(tf.float32, shape=(None, n + 1), name="X") File "/usr/local/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py", line 1507, in placeholder name=name) File "/usr/local/lib/python3.6/site-packages/tensorflow/python/ops/gen_array_ops.py", line 1997, in _placeholder name=name) File "/usr/local/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 768, in apply_op op_def=op_def) File "/usr/local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2336, in create_op original_op=self._default_original_op, op_def=op_def) File "/usr/local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1228, in init self._traceback = _extract_stack()

InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'X' with dtype float [[Node: X = Placeholderdtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]]

And I have no idea why. If I remove the line "print("MSE=%s" % (mse.eval()))", then everything will be fine. Any suggestion?

Thanks in advance!

gdelab gdelab · Accepted Answer · 2017-06-01T10:46:16

You can't evaluate your MSE without any data, you have to feed values for placeholders x and y to evaluate the mean square error between the prediction of the network on input x and the ground-truth labels given in y.

You can use

print("MSE=%s" % sess.run(mse, feed_dict={X:X_train, y:y_train}))

or do it while you're training:

_, mse_value = sess.run([training_op, mse], feed_dict={X:X_train, y:y_train})
print("MSE=%s" % mse_value)

Tensorflow - You must feed a value for placeholder tensor 'X' with dtype float

1 Answers