我安装Skflow并在Pycharm上运行digits.py示例并看到它返回错误“AttributeError:'module'对象没有属性'TensorFlowDNNRegressor”。我继续并在Ipython上运行相同的程序,一切都很好。应该是什么问题?
from sklearn import datasets, cross_validation, metrics
import tensorflow as tf
import skflow
from skflow import monitors
# Load dataset
digits = datasets.load_digits()
X = digits.images
y = digits.target
# Split it into train / test subsets
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)
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)
# Create a classifier, train and predict.
classifier = skflow.TensorFlowEstimator(model_fn=conv_model, n_classes=10,
steps=1000, learning_rate=0.05,
batch_size=128)
classifier.fit(X_train, y_train, val_monitor)
score = metrics.accuracy_score(y_test, classifier.predict(X_test))
print('Test Accuracy: {0:f}'.format(score))
此外,我了解到,当我在Ipython上工作时,我对Pycharm上Skflow的任何功能都有问题。对此有何猜测?
答案 0 :(得分:0)
您可以检查以确保Pycharm使用的是与ipython使用的相同的python解释器和环境。您可以在设置|项目|项目解释器
中的pycharm中设置解释器答案 1 :(得分:0)
TensorFlow在Pycharm中运作良好吗?如果是这样,您现在可以在其contrib模块中使用skflow安装每晚构建的TensorFlow 。
要安装每晚构建的TensorFlow,请参阅TensorFlow' Github页面上的README文件以获取说明。
然后您可以通过from tensorflow.contrib.skflow.python import skflow
导入skflow。
希望这会有所帮助。