Csv File Into Skflow

I'm just starting out with Tensorflow. As I understand it, SkFlow is a... Simplified interface for TensorFlow And for me simple is good. TensorFlow's Github has some useful start

Solution 1:

I was working on the same tutorial. I used scikit learn's cross_validation method to break the scikit Bunch object into train/test splits. Then just use those in the classifier.fit and classifier.evaluate methods.

from sklearn import cross_validation
import tensorflow as tf
import numpy as np
from sklearn import datasets

# load from scikit learn
iris = datasets.load_iris()
# break into train/test splits
x_train, x_test, y_train, y_test = cross_validation.train_test_split(
             iris.data, iris.target, test_size=0.2, random_state=42)

# commented out the previous loading code
'''
# Data sets
IRIS_TRAINING = "iris_training.csv"
IRIS_TEST = "iris_test.csv"
# Load datasets.
training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
    filename=IRIS_TRAINING,
    target_dtype=np.int,
    features_dtype=np.float32)
test_set = tf.contrib.learn.datasets.base.load_csv_with_header(
    filename=IRIS_TEST,
    target_dtype=np.int,
    features_dtype=np.float32)
'''
# Specify that all features have real-value data
feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]

# Build 3 layer DNN with 10, 20, 10 units respectively.
classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
                                        hidden_units=[10, 20, 10],
                                        n_classes=3,
                                        model_dir="./tmp/iris_model")

# Fit model. Add your train data here
classifier.fit(x=x_train,y=y_train,steps=2000)

# Evaluate accuracy. Add your test data here
accuracy_score = classifier.evaluate(x=x_test,y=y_test)["accuracy"]
print('Accuracy: {0:f}'.format(accuracy_score))

# Classify two new flower samples.
new_samples = np.array(
    [[6.4, 3.2, 4.5, 1.5], [5.8, 3.1, 5.0, 1.7]], dtype=float)
y = list(classifier.predict(new_samples, as_iterable=True))
print('Predictions: {}'.format(str(y)))