|
- import numpy as np
- import tensorflow as tf
-
-
- def tf_logreg(x, y_):
- '''
- Logistic Regression model in TensorFlow, for MNIST dataset.
-
- Parameters:
- x: Variable(tensorflow.python.framework.ops.Tensor), shape (N, dims)
- y_: Variable(tensorflow.python.framework.ops.Tensor), shape (N, num_classes)
- Return:
- loss: Variable(tensorflow.python.framework.ops.Tensor), shape (1,)
- y: Variable(tensorflow.python.framework.ops.Tensor), shape (N, num_classes)
- '''
-
- print("Build logistic regression model in tensorflow...")
- weight = tf.Variable(np.zeros(shape=(784, 10)).astype(np.float32))
- bias = tf.Variable(np.zeros(shape=(10, )).astype(np.float32))
- y = tf.matmul(x, weight) + bias
- loss = tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=y_)
- loss = tf.reduce_mean(loss)
- return loss, y
|