So I basically copypasted the code from the tensorflow tutorial adapted to this model:
Which tries to model a neural network to identify "stairs" shape, as it shows here:
import numpy as np
import tensorflow as tf
import _pickle as cPickle
with open("var_x.txt", "rb") as fp: # Unpickling
var_x = cPickle.load(fp)
with open("var_y.txt", "rb") as fp: # Unpickling
var_y = cPickle.load(fp)
# Declare list of features, we only have one real-valued feature
def model_fn(features, labels, mode):
# Build a linear model and predict values
W = tf.get_variable("W", [4], dtype=tf.float64)
b = tf.get_variable("b", [1], dtype=tf.float64)
y = tf.sigmoid( W*features['x'] + b)
# Loss sub-graph
loss = tf.reduce_sum(tf.square(y - labels))
# Training sub-graph
global_step = tf.train.get_global_step()
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = tf.group(optimizer.minimize(loss),
tf.assign_add(global_step, 1))
# EstimatorSpec connects subgraphs we built to the
# appropriate functionality.
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=y,
loss=loss,
train_op=train)
estimator = tf.estimator.Estimator(model_fn=model_fn)
# define our data sets
x_train = np.array(var_x)
y_train = np.array(var_y)
input_fn = tf.estimator.inputs.numpy_input_fn(
{"x": x_train}, y_train, batch_size=4, num_epochs=10, shuffle=True)
# train
estimator.train(input_fn=input_fn, steps=1000)
# Here we evaluate how well our model did.
print(estimator.get_variable_value("b"))
print(estimator.get_variable_value("W"))
new_samples = np.array(
[255., 1., 255., 255.], dtype=np.float64)
predict_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": new_samples},
num_epochs=1,
shuffle=False)
predictions = list(estimator.predict(input_fn=predict_input_fn))
print(predictions)
The problem is that when I try to predict a figure that should clearly be a stairs: [255., 1., 255., 255.] I get a "ValueError: None values not supported.". The training works just fine (save for the facts that the weights it finds are not very similar to the ones here: http://blog.kaggle.com/2017/11/27/introduction-to-neural-networks/). But the predict method doesn't work. This code is mustly just a copy of the tensorflow example, adapted to a four dimensional vector for x.