TensorFlowScorer Class
Transforms the data using the TensorFlow model.
- Inheritance
-
nimbusml.internal.core.preprocessing._tensorflowscorer.TensorFlowScorerTensorFlowScorernimbusml.base_transform.BaseTransformTensorFlowScorersklearn.base.TransformerMixinTensorFlowScorer
Constructor
TensorFlowScorer(model_location, input_columns=None, output_columns=None, batch_size=64, add_batch_dimension_inputs=False, columns=None, **params)Parameters
- columns
see Columns.
- model_location
TensorFlow model used by the transform. Please see https://www.tensorflow.org/mobile/prepare_models for more details.
- input_columns
The names of the model inputs.
- output_columns
The name of the outputs.
- batch_size
Number of samples to use for mini-batch training.
- add_batch_dimension_inputs
Add a batch dimension to the input e.g. input = [224, 224, 3] => [-1, 224, 224, 3].
- params
Additional arguments sent to compute engine.
Examples
###############################################################################
# TensorFlowScorer
import os
from nimbusml import FileDataStream
from nimbusml.preprocessing import TensorFlowScorer
import pandas as pd
import numpy as np
data = pd.DataFrame(np.ones(9) * 1.1)
data.to_csv("test.csv", index=False)
data = FileDataStream.read_csv("test.csv",
schema="col=a:R4:0-3 "
"col=b:R4:4-7")
# In the model file,
# inputs are two vector with dimension 4
data.head()
# transform usage
xf = TensorFlowScorer(
model_location=os.path.join(os.path.dirname(__file__), 'frozen_saved_model.pb'),
columns={'c': ['a', 'b']}
)
# fit and transform
features = xf.fit_transform(data)
print(features)
# a.0 a.1 a.2 a.3 b.0 b.1 b.2 b.3 ...
# 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
# 1 1.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
# 2 1.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
# 3 1.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
# 4 1.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
# 5 1.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
# 6 1.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
# 7 1.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
# 8 1.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
# 9 1.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
Remarks
The TensorflowTransform extracts the specified outputs from the operations computed on the graph (given the input(s)) using a pre-trained TensorFlow model. The transform takes as input the Tensorflow model together with the names of the inputs to the model and names of the operations for which output values will be extracted from the model.
This transform has the floowing assumptions regarding the input, output and processing of data:
The transform currently accepts the
frozen TensorFlow model as the input.
The name of input column(s) should match the name
of input(s) in Tensorflow model.
The name of each output column should match one of the
operations in the Tensorflow graph.
Methods
| get_params |
Get the parameters for this operator. |
get_params
Get the parameters for this operator.
get_params(deep=False)Parameters
- deep