python - TF: Fetch argument x has invalid type <type 'numpy.float32'>, must be a string or Tensor. (Can not convert a float32 into a Tensor or Operation.) -


i have time series model in tf. it's simple auto-regressive model.

the original y vector of length 100 (n).

i float not tensor error (as per subject). @ second instance though. 

lr = .01 steps = 100  def net(x, w, b):   # x has 2 previous values   x = [x[-1], x[-2], x[-1] - x[-2]]   x = tf.reshape(x, [1, 3])   x = tf.add(tf.matmul(x, w[0]), b[0])   pred = tf.add(tf.matmul(x, w[1]), b[1])   return pred  y_data = y - np.mean(y)  x = tf.placeholder(tf.float32, [2], name='x') y = tf.placeholder(tf.float32, [1], name='y') w = [tf.variable(tf.random_normal([3, 3])), tf.variable(tf.random_normal([3, 1]))] b = [tf.variable(tf.random_normal([1])), tf.variable(tf.random_normal([1]))] pred = net(x, w, b) cost = tf.sqrt(tf.reduce_mean(tf.square(tf.subtract(pred, y)))) optimizer = tf.train.adamoptimizer(learning_rate=lr).minimize(cost)  init = tf.global_variables_initializer() tf.session() sess:   sess.run(init)   step in range(steps):     # random samples of data     ts = np.random.choice(np.arange(2, n), int(n * .5), replace=false)     t in ts:       x_data = [y_data[t - 2], y_data[t - 1]]       y_data_cur = [y_data[t]]       print(x_data, y_data_cur, x, y, pred)       _, cost, p = sess.run([optimizer, cost, pred], feed_dict={x: x_data, y: y_data_cur})       print(cost, p)     if step % 10 == 0:       print(step, cost)

when run model:

_, cost, p = sess.run([optimizer, cost, pred], feed_dict={x: x_data, y: y_data_cur})

you overwriting cost variable, used hold tensorflow tensor cost, evaluated value, next iteration fails. change name of variable:

_, cost_val, p = sess.run([optimizer, cost, pred], feed_dict={x: x_data, y: y_data_cur})

and of course replace cost cost_val in print statements.


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