Feature extraction Tensorflow Estimator

Question

I am pretty new in using Tensorflow.

I followed the tutorial on how to create a CNN to classifier for MNIST dataset. I want to know if there is a way to extract features, i.e. values of first FC layer, using Estimator class.

Here is my code.

from __future__ import absolute_import, division, print_function
import tensorflow as tf
import numpy as np

tf.logging.set_verbosity(tf.logging.INFO)

def cnn_model_fn(features, labels, mode):
    # Input Layer
    input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])

    # Convolutional Layer #1
    conv1 = tf.layers.conv2d(inputs=input_layer, filters=32, kernel_size=[5, 5], padding="same", activation=tf.nn.relu)

    # Pooling Layer #1
    pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)

    # Convolutional Layer #2 and Pooling Layer #2
    conv2 = tf.layers.conv2d(inputs=pool1, filters=64, kernel_size=[5, 5], padding="same", activation=tf.nn.relu)
    pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)

    # Dense Layer
    pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
    dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
    dropout = tf.layers.dropout(inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)

    # Logits Layer
    logits = tf.layers.dense(inputs=dropout, units=10)

    predictions = {
        # Generate predictions (for PREDICT and EVAL mode)
        "classes": tf.argmax(input=logits, axis=1),
        # Add `softmax_tensor` to the graph. It is used for PREDICT and by the `logging_hook`.
        "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
    }

    if mode == tf.estimator.ModeKeys.PREDICT:
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)

    # Calculate Loss (for both TRAIN and EVAL modes)
    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)

    # Configure the Training Op (for TRAIN mode)
    if mode == tf.estimator.ModeKeys.TRAIN:
        optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
        train_op = optimizer.minimize(loss=loss, global_step=tf.train.get_global_step())
        return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)

    # Add evaluation metrics (for EVAL mode)
    eval_metric_ops = {"accuracy": tf.metrics.accuracy(labels=labels, predictions=predictions["classes"])}
    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)

The main function is defined that way:

    if __name__ == "__main__":
        # Load training and eval data
        mnist = tf.contrib.learn.datasets.load_dataset("mnist")
        train_data = mnist.train.images  # Returns np.array
        train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
        eval_data = mnist.test.images  # Returns np.array
        eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)

        # Create the Estimator
        mnist_classifier = tf.estimator.Estimator(model_fn=cnn_model_fn, model_dir="mnist_convnet")

        # Set up logging for predictions
        tensors_to_log = {"probabilities": "softmax_tensor"}
        logging_hook = tf.train.LoggingTensorHook(tensors=tensors_to_log, every_n_iter=50)

        # Train the model
        # train_input_fn = tf.estimator.inputs.numpy_input_fn(
        #     x={"x": train_data},
        #     y=train_labels,
        #     batch_size=100,
        #     num_epochs=None,
        #     shuffle=True)
        # mnist_classifier.train(input_fn=train_input_fn, steps=20000, hooks=[logging_hook])

        # Evaluate the model and print results
        eval_input_fn = tf.estimator.inputs.numpy_input_fn(
            x={"x": eval_data},
            y=eval_labels,
            num_epochs=1,
            shuffle=False)
        eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
        print(eval_results)

        print(mnist_classifier.get_variable_names())

Answer 1

You could add values you are interested in to the predictions dictionary returned in predict mode. E.g. in your case just add something like "fc_layer": dense to predictions; then when you run estimator.predict you will get those values returned along with the probabilities and classes.

Edit: predict returns a generator over results. Here's an example of how to use it:

pred_generator = estimator.predict(input_fn=eval_input_fn)
for pred_dict in pred_generator:
    print(pred_dict["classes"])
    print(pred_dict["fc_layer"])
    ....