tf.contrib.learn.TensorFlowRNNClassifier.predict_proba()

tf.contrib.learn.TensorFlowRNNClassifier.predict_proba(x, batch_size=None) Predict class probability of the input samples x

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tf.contrib.learn.ModeKeys

class tf.contrib.learn.ModeKeys Standard names for model modes. The following standard keys are

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tf.contrib.learn.TensorFlowEstimator.

tf.contrib.learn.TensorFlowEstimator.__repr__()

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tf.contrib.learn.LinearRegressor.set_params()

tf.contrib.learn.LinearRegressor.set_params(**params) Set the parameters of this estimator. The

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tf.contrib.learn.LinearRegressor.fit()

tf.contrib.learn.LinearRegressor.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None) See

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tf.contrib.learn.DNNRegressor.evaluate()

tf.contrib.learn.DNNRegressor.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None) See

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tf.contrib.learn.TensorFlowEstimator.evaluate()

tf.contrib.learn.TensorFlowEstimator.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)

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tf.contrib.learn.TensorFlowRNNClassifier.

tf.contrib.learn.TensorFlowRNNClassifier.__repr__()

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tf.contrib.learn.TensorFlowRNNClassifier.weights_

tf.contrib.learn.TensorFlowRNNClassifier.weights_ Returns weights of the rnn layer.

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tf.contrib.learn.TensorFlowEstimator.predict()

tf.contrib.learn.TensorFlowEstimator.predict(x, axis=1, batch_size=None) Predict class or regression for x.

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