Methods to Move Data from MongoDB to SQL Server.Why do you need to move data from MongoDB to SQL server?.Note: Since the goal of this tutorial is to demonstrate Tensorflow-IO's capability to prepare tf.data.Datasets from mongodb and train tf.keras models directly, improving the accuracy of the models is out of the current scope. Infer on the test data res = model.evaluate(test_ds)ġ09/109 - 0s 2ms/step - loss: 0.5696 - accuracy: 0.7383
Model = tf.keras.Model(all_inputs, output) X = tf.(32, activation="relu")(all_features) # Convert the feature columns into a tf.keras layerĪll_features = tf.(encoded_features) Normalization_layer = get_normalization_layer(header, train_ds)Įncoded_numeric_col = normalization_layer(numeric_col)Įncoded_features.append(encoded_numeric_col)īuild, compile and train the model # Set the parameters Numeric_col = tf.keras.Input(shape=(1,), name=header) # Prepare a Dataset that only yields our feature.įeature_ds = dataset.map(lambda x, y: x) Normalizer = preprocessing.Normalization(axis=None) # Create a Normalization layer for our feature. However, the standard feature_columns can also be used.įor a better understanding of the preprocessing_layers in classifying structured data, please refer to the structured data tutorial def get_normalization_layer(name, dataset): Test_ds = test_ds.map(lambda v: (v, v.pop("target")))Ĭonnection successful: mongodb://localhost:27017Īs per the structured data tutorial, it is recommended to use the Keras Preprocessing Layers as they are more intuitive, and can be easily integrated with the models. Uri=URI, database=DATABASE, collection=TEST_COLLECTION Train_ds = train_ds.map(lambda v: (v, v.pop("target"))) Validate tf and tfio imports print("tensorflow-io version:
MONGODB GENERATE TEST DATA INSTALL
Install the required tensorflow-io and mongodb (helper) packages pip install -q tensorflow-io pip install -q pymongo Import packages import osįrom sklearn.model_selection import train_test_splitįrom import preprocessing This tutorial uses pymongo as a helper package to create a new mongodb database and collection to store the data. Note: A basic understanding of mongodb storage will help you in following the tutorial with ease. This tutorial focuses on preparing tf.data.Datasets by reading data from mongoDB collections and using it for training a tf.keras model.
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