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[英]Tensorflow feed_dict with list of floats and InvalidArgumentError
[英]TensorFlow “InvalidArgumentError” for value fed to placeholder via feed_dict
我正在研究TensorFlow的示例問題(特別是與占位符一起使用),並且在我相當確定那些形狀/類型錯誤時,我不明白為什么會收到(看起來是)形狀/類型錯誤。
我嘗試使用X_batch和y_batch中的各種浮點類型,嘗試將大小從“無”(未指定)更改為將要傳遞的大小(100),但都沒有用
import tensorflow as tf
import numpy as np
from sklearn.datasets import fetch_california_housing
def fetch_batch(epoch, batch_index, batch_size, X, y):
np.random.seed(epoch * batch_index)
indices = np.random.randint(m, size=batch_size)
X_batch = X[indices]
y_batch = y[indices]
return X_batch.astype('float32'), y_batch.astype('float32')
if __name__ == "__main__":
housing = fetch_california_housing()
m, n = housing.data.shape
# standardizing input data
standardized_housing = (housing.data - np.mean(housing.data)) / np.std(housing.data)
std_housing_bias = np.c_[np.ones((m, 1)), standardized_housing]
# using the size "n+1" to account for the bias term
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, 1), dtype=tf.float32, name='theta')
y_pred = tf.matmul(X, theta, name='predictions')
error = y_pred - y
mse = tf.reduce_mean(tf.square(error), name='mse')
n_epochs = 1000
learning_rate = 0.01
batch_size = 100
n_batches = int(np.ceil(m / batch_size))
# using the Gradient Descent Optimizer class from tensorflow's optimizer selection
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
training_op = optimizer.minimize(mse)
# creates a node in the computational graph that initializes all variables when it is run
init = tf.global_variables_initializer()
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, std_housing_bias, \
housing.target.reshape(-1, 1))
print(X_batch.shape, X_batch.dtype, y_batch.shape, y_batch.dtype)
sess.run(training_op, feed_dict={X: X_batch, y: y_batch})
if epoch % 100 == 0:
print(f"Epoch {epoch} MSE = {mse.eval()}")
best_theta = theta.eval()
print("Mini Batch Gradient Descent Beta Estimates")
print(best_theta)
我得到的錯誤是:
InvalidArgumentError: You must feed a value for placeholder tensor 'X' with dtype float and shape [?,9]
[[node X (defined at /Users/marshallmcquillen/Scripts/lab.py:25) ]]
我拋出了一個打印X_batch和y_batch屬性的打印語句,這些是我期望的,但仍然無法使用。
該mse
要評估也取決於占位符X
和y
因此,你需要與提供feed_dict
為好。 您可以通過將行更改為
if epoch % 100 == 0:
print(f"Epoch {epoch} MSE = {mse.eval(feed_dict={X: X_batch, y: y_batch})}")
但是由於您正在嘗試評估模型,因此使用測試數據集是合理的。 所以理想情況下
if epoch % 100 == 0:
print(f"Epoch {epoch} MSE = {mse.eval(feed_dict={X: X_test, y: y_test})}")
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