How to predict using trained Tensorflow model

Question

I have created and trained a neural network but I would like to be able to input test points and see its results (rather than using an eval function).

The model runs fine and the cost reduces every epoch, but I just want to add a line at the end to pass some input coordinates and have it tell me the predicted transformed coordinates.

import tensorflow as tf
import numpy as np

def coordinate_transform(size, angle):
    input = np.random.rand(size, 2)
    output = np.zeros((size, 2))
    noise = 0.05*(np.add(np.random.rand(size) * 2, -1))
    theta = np.add(np.add(np.arctan(input[:,1] / input[:,0]) , angle) , noise)
    radii = np.sqrt(np.square(input[:,0]) + np.square(input[:,1]))
    output[:,0] = np.multiply(radii, np.cos(theta))
    output[:,1] = np.multiply(radii, np.sin(theta))
    return input, output

#Data
input, output = coordinate_transform(2000, np.pi/2)
train_in = input[:1000]
train_out = output[:1000]
test_in = input[1000:]
test_out = output[1000:]

# Parameters
learning_rate = 0.001
training_epochs = 15
batch_size = 1
display_step = 1

# Network Parameters
n_hidden_1 = 100 # 1st layer number of features
n_input = 2 # [x,y]
n_classes = 2 # output x,y coords

# tf Graph input
x = tf.placeholder("float", [1,n_input])
y = tf.placeholder("float", [1, n_input])

# Create model
def multilayer_perceptron(x, weights, biases):
    # Hidden layer with RELU activation
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
    layer_1 = tf.nn.relu(layer_1)
    # Output layer with linear activation
    out_layer = tf.matmul(layer_1, weights['out']) + biases['out']
    return out_layer

# Store layers weight & bias
weights = {
    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
    'out': tf.Variable(tf.random_normal([n_hidden_1, n_classes]))
}
biases = {
    'b1': tf.Variable(tf.random_normal([n_hidden_1])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}

# Construct model
pred = multilayer_perceptron(x, weights, biases)

# Define loss and optimizer
#cost = tf.losses.mean_squared_error(0, (tf.slice(pred, 0, 1) - x)**2 + (tf.slice(pred, 1, 1) - y)**2)
cost = tf.losses.mean_squared_error(y, pred)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
optimizer = optimizer.minimize(cost)

# Initializing the variables
#init = tf.global_variables_initializer()
init = tf.initialize_all_variables()

# Launch the graph
with tf.Session() as sess:
    sess.run(init)

    # Training cycle
    for epoch in range(training_epochs):
        avg_cost = 0.
        total_batch = 1000#int(len(train_in)/batch_size)
        # Loop over all batches
        for i in range(total_batch):
            batch_x = train_in[i].reshape((1,2))
            batch_y = train_out[i].reshape((1,2))

            #print(batch_x.shape)
            #print(batch_y.shape)
            #print(batch_y, batch_x)
            # Run optimization op (backprop) and cost op (to get loss value)
            _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,
                                                          y: batch_y})
            # Compute average loss
            avg_cost += c / total_batch
        # Display logs per epoch step
        if epoch % display_step == 0:
            print ("Epoch:", '%04d' % (epoch+1), "cost=", \
                "{:.9f}".format(avg_cost))
    print("Optimization Finished!")

    # Test model
    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    # Calculate accuracy
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

    #Make predictions

Answer 1

Well the 'pred' op is your actual outcome (as it's used to compare with y when calculating the loss), so something like the following should do the trick:

print(sess.run([pred], feed_dict={x: _INPUT_GOES_HERE_ })

Obviously _INPUT_GOES_HERE_ will need to be replaced by the actual input.

Answer 2

You can also use the tensorflow.python.saved_model libs to save your model in a format that can be served by TensorFlow Serving.

TensorFlow Serving recently became a whole lot easier to install and setup:

https://github.com/tensorflow/serving/blob/master/tensorflow_serving/g3doc/setup.md#installing-using-apt-get

Below is some sample code (you'll need to adjust the feeds/inputs and fetches/outputs for your use case).

Create SignatureDef for your model:

from tensorflow.python.saved_model import utils
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import signature_def_utils

graph = tf.get_default_graph()

x_observed = graph.get_tensor_by_name('x_observed:0')
y_pred = graph.get_tensor_by_name('add:0')

tensor_info_x_observed = utils.build_tensor_info(x_observed)
print(tensor_info_x_observed)

tensor_info_y_pred = utils.build_tensor_info(y_pred)
print(tensor_info_y_pred)

prediction_signature = signature_def_utils.build_signature_def(inputs = 
                {'x_observed': tensor_info_x_observed}, 
                outputs = {'y_pred': tensor_info_y_pred}, 
                method_name = signature_constants.PREDICT_METHOD_NAME)

Using SaveModelBuilder to save your model with the SignatureDef assets defined above:

from tensorflow.python.saved_model import builder as saved_model_builder
from tensorflow.python.saved_model import tag_constants

unoptimized_saved_model_path = '/root/models/linear_unoptimized/cpu/%s' % version
print(unoptimized_saved_model_path)

builder = saved_model_builder.SavedModelBuilder(unoptimized_saved_model_path)
builder.add_meta_graph_and_variables(sess, 
                                     [tag_constants.SERVING],
                                     signature_def_map={'predict':prediction_signature,                                     
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:prediction_signature}, 
                                     clear_devices=True,
)

builder.save(as_text=False)

More details in the github and docker repos referenced here: http://pipeline.ai