valid_cnt = int(train.shape[0] * 0.1)
test_idx, training_idx = indices[:valid_cnt],\
indices[valid_cnt:]
test, train = train[test_idx,:],\
train[training_idx,:]
test_labels, train_labels = labels[test_idx],\
labels[training_idx]
sess = tf.InteractiveSession()
# Logistic Regression
# steps is number of total batches
# steps/batch_size = num_epochs
classifier = skflow.TensorFlowLinearClassifier(
n_classes=5,
steps=1000,
optimizer='Adam',
learning_rate=0.01,
continue_training=True)
# One line training
classifier.fit(train.reshape([-1,36*36]),train_labels)
# sklearn compatible accuracy
sklearn.metrics.accuracy_score(test_labels,
classifier.predict(test.reshape([-1,36*36])))
# Dense neural net
classifier = skflow.TensorFlowDNNClassifier(
hidden_units=[10,5],
n_classes=5,
steps=1000,
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.contrib import skflow
### Download and load MNIST data.
#mnist = skflow.datasets.load_dataset('mnist')
from tensorflow.examples.tutorials.mnist import input_data
### Download and load MNIST data.
mnist = input_data.read_data_sets('MNIST_data')
### Linear classifier.
classifier = skflow.TensorFlowLinearClassifier(
n_classes=10, batch_size=100, steps=1000, learning_rate=0.01)
classifier.fit(mnist.train.images, mnist.train.labels)
score = metrics.accuracy_score(mnist.test.labels, classifier.predict(mnist.test.images))
print('Accuracy: {0:f}'.format(score))
### Convolutional network
def max_pool_2x2(tensor_in):
return tf.nn.max_pool(tensor_in, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
padding='SAME')
def conv_model(X, y):
# reshape X to 4d tensor with 2nd and 3rd dimensions being image width and height
# final dimension being the number of color channels
X = tf.reshape(X, [-1, 28, 28, 1])
# first conv layer will compute 32 features for each 5x5 patch
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import shutil
from sklearn import datasets, metrics, cross_validation
from tensorflow.contrib import skflow
iris = datasets.load_iris()
X_train, X_test, y_train, y_test = cross_validation.train_test_split(
iris.data, iris.target, test_size=0.2, random_state=42)
classifier = skflow.TensorFlowLinearClassifier(n_classes=3)
classifier.fit(X_train, y_train)
score = metrics.accuracy_score(y_test, classifier.predict(X_test))
print('Accuracy: {0:f}'.format(score))
# Clean checkpoint folder if exists
try:
shutil.rmtree('/tmp/skflow_examples/iris_custom_model')
except OSError:
pass
# Save model, parameters and learned variables.
classifier.save('/tmp/skflow_examples/iris_custom_model')
classifier = None
## Restore everything