Created
July 3, 2020 07:00
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| import tensorflow as tf | |
| from tensorflow.keras.backend import placeholder | |
| print(tf.__version__) | |
| print(tf.executing_eagerly()) | |
| x = placeholder(shape=(1, 1), dtype=tf.float32, name='x') | |
| t = placeholder(shape=(1, 1), dtype=tf.float32, name='t') | |
| W0 = tf.Variable(tf.random.normal((1,20)), dtype=tf.float32) | |
| W1 = tf.Variable(tf.random.normal((2,20)), dtype=tf.float32) | |
| W2 = tf.Variable(tf.random.normal((20,1)), dtype=tf.float32) | |
| # Working basic version | |
| with tf.GradientTape() as g: | |
| g.watch(x) | |
| inpt = x | |
| print(inpt) | |
| ur = tf.sigmoid(tf.matmul(inpt, W0)) | |
| ur = tf.sigmoid(tf.matmul(ur, W2)) | |
| u_t = g.gradient(ur, x) | |
| print(u_t) | |
| # Not working version | |
| with tf.GradientTape() as g: | |
| g.watch(x) | |
| g.watch(t) | |
| inpt = tf.concat([x,t], 1) | |
| print(inpt) | |
| ur = tf.sigmoid(tf.matmul(inpt, W1)) | |
| ur = tf.sigmoid(tf.matmul(ur, W2)) | |
| u_t = g.gradient(ur, x) | |
| print(u_t) |
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