single prediction for a neural network ( tensorflow, eager execution )

Question

I'm quite new to tensorflow and python and I'm working on a neural network that is able to classify the sounds of the Urban Sound dataset (https://urbansounddataset.weebly.com/urbansound8k.html).

I used the crash course on machine learning tutorial (https://colab.research.google.com/notebooks/mlcc/multi-class_classification_of_handwritten_digits.ipynb?utm_source=mlcc&utm_campaign=colab-external&utm_medium=referral&utm_content=multiclass-colab&hl=en) as a base for my program, and the model is quite good, but now I want to analyse the prediction for different audio files.

first I create the classifier in a file, then (code below) I recall the model and compute the check on the test dataset I left behind

import pandas as pd
import numpy as np
import time
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from tensorflow.python.data import Dataset
from sklearn import metrics 

urbansound_dataframe = pd.read_csv("C:\\Users\\UrbanSound8K\\metadata\\dataset_train_fin.csv", sep=',')

urbansound_dataframe_test = pd.read_csv("C:\\Users\\UrbanSound8K\\metadata\\dataset_test_fin.csv", sep=',')


def construct_feature_columns(input_features):
    return set([tf.feature_column.numeric_column(my_feature)
                for my_feature in input_features])

def parse_labels_and_features(dataset):
    """Args:
      dataset: A Pandas `Dataframe`, containing the label on the last column
    Returns:
      A `tuple` `(labels, features)`:
        labels: A Pandas `Series`.
        features: A Pandas `DataFrame`.
    """
    labels = dataset['classID']
    features = dataset[[                               ###metto i nomi delle features che voglio utilizzare
        "mfcc_1", "mfcc_2", "mfcc_3", "mfcc_4", "mfcc_5", "mfcc_6", "mfcc_7", "mfcc_8", "mfcc_9", "mfcc_10", "mfcc_11",
        "mfcc_12", "mfcc_13", "mfcc_14", "mfcc_15", "mfcc_16", "mfcc_17", "mfcc_18", "mfcc_19", "mfcc_20", "mfcc_21",
        "mfcc_22", "mfcc_23", "mfcc_24", "mfcc_25", "mfcc_26", "mfcc_27", "mfcc_28", "mfcc_29", "mfcc_30", "mfcc_31",
        "mfcc_32", "mfcc_33", "mfcc_34", "mfcc_35", "mfcc_36", "mfcc_37", "mfcc_38", "mfcc_39", "mfcc_40"
    ]]    
    return labels, features

def create_predict_input_fn(features, labels, batch_size):
    """Args:
      features: The features to base predictions on.
      labels: The labels of the prediction examples.

    Returns:
      A function that returns features and labels for predictions.
    """

    def _input_fn():
        raw_features = {key: np.array(value) for key, value in dict(features).items()}
        raw_targets = np.array(labels)

        ds = Dataset.from_tensor_slices((raw_features, raw_targets))  
        ds = ds.batch(batch_size)

        # Return the next batch of data.
        feature_batch, label_batch = ds.make_one_shot_iterator().get_next()
        return feature_batch, label_batch

    return _input_fn


percentage = 75   
training_targets, training_examples = parse_labels_and_features(urbansound_dataframe.head(int(((len(urbansound_dataframe)*percentage)/100))))

validation_targets, validation_examples = parse_labels_and_features(urbansound_dataframe.tail(len(urbansound_dataframe)-int(((len(urbansound_dataframe)*percentage)/100))))



learning_rate=0.004

my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)
my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)

hidden_units=[2000,2000,2000]



my_classifier = tf.estimator.DNNClassifier(feature_columns=construct_feature_columns(training_examples),
                                            n_classes=11,
                                            hidden_units=hidden_units,
                                            optimizer=my_optimizer,
                                            config=tf.contrib.learn.RunConfig(keep_checkpoint_max=1),
                                            model_dir="C:\\Users\\PycharmProjects\\MODEL_UrbanSound_05_NN")




###test on unused directories
test_targets, test_examples = parse_labels_and_features(urbansound_dataframe_test)

predict_test_input_fn = create_predict_input_fn(test_examples, test_targets, batch_size=400)

test_predictions = my_classifier.predict(input_fn=predict_test_input_fn)
test_predictions = np.array([item['class_ids'][0] for item in test_predictions])

accuracy = metrics.accuracy_score(test_targets, test_predictions)

print("Accuracy on test data: ", (accuracy*100),"%")
print("fine test")

# Output a plot of the confusion matrix.
cm = metrics.confusion_matrix(test_targets, test_predictions)
# Normalize the confusion matrix by row (i.e by the number of samples
# in each class).
cm_normalized = cm.astype("float") / cm.sum(axis=1)[:, np.newaxis]
ax = sns.heatmap(cm_normalized, cmap="bone_r")
ax.set_aspect(1)
plt.title("Confusion matrix on test data")
plt.ylabel("True label")
plt.xlabel("Predicted label")
plt.show()

my main question here is how to transform the code to predict a single file, having the features of the file extracted by another part of the program. I think _I ave to work on the input_fn

 def _input_fn():
    raw_features = {key: np.array(value) for key, value in dict(features).items()}
    raw_targets = np.array(labels)

    ds = Dataset.from_tensor_slices((raw_features, raw_targets)) 
    ds = ds.batch(batch_size)

    # Return the next batch of data.
    feature_batch, label_batch = ds.make_one_shot_iterator().get_next()
    return feature_batch, label_batch

return _input_fn

but I don't know how to change it. any help is welcome, thank you.