from sklearn import datasets, cross_validation, metrics
import tensorflow as tf

import skflow
# from skflow import monitors


digits = datasets.load_digits()
X = digits.images
y = digits.target

X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.2, random_state=42)

# Split X_train again to create validation data
# X_train, X_val, y_train, y_val = cross_validation.train_test_split(X_train, y_train, test_size=0.2, random_state=42)

# TensorFlow model using Scikit Flow ops
def conv_model(X, y):
  X = tf.expand_dims(X, 3)
  # convolution with max pooling
  features = tf.reduce_max(skflow.ops.conv2d(X, 12, [3,3]), [1,2])
  features = tf.reshape(features, [-1,12])
  return skflow.models.logistic_regression(features, y)

# val_monitor = monitors.ValidationMonitor(X_val, y_val, n_classes=10, print_steps=50)

classifier = skflow.TensorFlowEstimator(model_fn=conv_model, n_classes=10, steps=500, learning_rate=0.05, batch_size=128)
# classifier.fit(X_train, y_train, val_monitor)
classifier.fit(X_train, y_train)
score = metrics.accuracy_score(y_test, classifier.predict(X_test))
print('Test Accuracy: {0:f}'.format(score))
