Tensorflow classifier.evaluate running indefinitely? - python-3.x

Question

I've started trying out some of the Tensorflow API's. I am using the iris data set to experiment with Tensorflows Estimator's. I'm loosely following this tutorial except that I load my data in a little differently: https://www.tensorflow.org/guide/premade_estimators#top_of_page
My problem is that when the code below executes and I get to the section with:
# Evaluate the model.
eval_result = classifier.evaluate(
My computer just runs seemingly without end. I've been waiting for my jupyter notebook to complete this step now for an hour and a half but no end in sight. The lastoutput of the notebook is:
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
Problem statement: How can I adjust my code to make this evaluation more efficient I'm obviously making it do much more work than I anticipated.
So far I have tried adjusting the batch size and the number or neurons in the layers but with no luck.
#First we want to import what we need. Typically this will be some combination of:
import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
%matplotlib inline
#Extract the data from the iris dataset.
df = pd.read_csv('IRIS.csv')
le = LabelEncoder()
df['species'] = le.fit_transform(df['species'])
#Extract both into features and labels.
#features should be a dictionary.
#label can just be an array
def extract_features_and_labels(dataframe):
#features and label for training
x = dataframe.copy()
y = dataframe.pop('species')
return dict(x), y
#break the data up into train and test.
#split the overall df into training and testing data
train, test = train_test_split(df, test_size=0.2)
train_x, train_y = extract_features_and_labels(train)
test_x, test_y = extract_features_and_labels(test)
print(len(train_x), 'training examples')
print(len(train_y), 'testing examples')
my_feature_columns = []
for key in train_x.keys():
my_feature_columns.append(tf.feature_column.numeric_column(key=key))
def train_input_fn(features, labels, batch_size):
"""An input function for training"""
# Convert the inputs to a Dataset.
dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
# Shuffle, repeat, and batch the examples.
return dataset.shuffle(1000).repeat().batch(batch_size)
#Build the classifier!!!!
classifier = tf.estimator.DNNClassifier(
feature_columns=my_feature_columns,
# Two hidden layers of 10 nodes each.
hidden_units=[4, 4],
# The model must choose between 3 classes.
n_classes=3)
# Train the Model.
classifier.train(
input_fn=lambda:train_input_fn(train_x, train_y, 10), steps=1000)
# Evaluate the model.
eval_result = classifier.evaluate(
input_fn=lambda:train_input_fn(test_x, test_y, 100))
print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result))

Answer 1

Had to switch a lot of things up but finally got the estimator working on the IRIS data set. Here is the code below for any who may find it useful in the future. Cheers.
#First we want to import what we need. Typically this will be some combination of:
import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn import preprocessing
%matplotlib inline
#Extract the data from the iris dataset.
df = pd.read_csv('IRIS.csv')
#Grab only our categorial data
#categories = df.select_dtypes(include=[object])
le = LabelEncoder()
df['species'] = le.fit_transform(df['species'])
# use df.apply() to apply le.fit_transform to all columns
#X_2 = X.apply(le.fit_transform)
#Reshape as the one hot encoder doesnt like one row/column
#X_2 = X.reshape(-1, 1)
#features is everything but our label so makes sense to simply....
features = df.drop('species', axis=1)
print(features.head())
target = df['species']
print(target.head())
#trains and test
X_train, X_test, y_train, y_test = train_test_split(
features, target, test_size=0.33, random_state=42)
#Introduce Tensorflow feature column (numeric column)
numeric_column = ['sepal_length','sepal_width','petal_length','petal_width']
numeric_features = [tf.feature_column.numeric_column(key = column)
for column in numeric_column]
print(numeric_features[0])
#Build the input function for training
training_input_fn = tf.estimator.inputs.pandas_input_fn(x = X_train,
y=y_train,
batch_size=10,
shuffle=True,
num_epochs=None)
#Build the input function for testing input
eval_input_fn = tf.estimator.inputs.pandas_input_fn(x=X_test,
y=y_test,
batch_size=10,
shuffle=False,
num_epochs=1)
#Instansiate the model
dnn_classifier = tf.estimator.DNNClassifier(feature_columns=numeric_features,
hidden_units=[3,3],
optimizer=tf.train.AdamOptimizer(1e-4),
n_classes=3,
dropout=0.1,
model_dir = "dnn_classifier")
dnn_classifier.train(input_fn = training_input_fn,steps=2000)
#Evaluate the trained model
dnn_classifier.evaluate(input_fn = eval_input_fn)
# print("Loss is " + str(loss))
pred = list(dnn_classifier.predict(input_fn = eval_input_fn))
for e in pred:
print(e)
print("\n")
#pred = [p['species'][0] for p in pred]