def build_new_deep_neural_network_from_env_variables():
csv_file = ev(<EOL>"<STR_LIT>",<EOL>"<STR_LIT>")<EOL>meta_file = ev(<EOL>"<STR_LIT>",<EOL>"<STR_LIT>")<EOL>predict_feature = ev(<EOL>"<STR_LIT>",<EOL>"<STR_LIT>")<EOL>test_size = float(ev(<EOL>"<STR_LIT>",<EOL>"<STR_LIT>"))<EOL>if not os.path.exists(csv_file):<EOL><INDENT>log.error(("<STR_LIT>")<EOL>.format(csv_file))<EOL>sys.exit(<NUM_LIT:1>)<EOL><DEDENT>res = build_training_request(<EOL>csv_file=csv_file,<EOL>meta_file=meta_file,<EOL>predict_feature=predict_feature,<EOL>test_size=test_size)<EOL>if res["<STR_LIT:status>"] != VALID:<EOL><INDENT>log.error(("<STR_LIT>"<EOL>"<STR_LIT>")<EOL>.format(<EOL>res["<STR_LIT:status>"],<EOL>res["<STR_LIT>"]))<EOL>sys.exit(<NUM_LIT:1>)<EOL><DEDENT>else:<EOL><INDENT>log.info(("<STR_LIT>"<EOL>"<STR_LIT>")<EOL>.format(<EOL>res["<STR_LIT:status>"],<EOL>res["<STR_LIT>"],<EOL>res["<STR_LIT>"]))<EOL><DEDENT>log.info("<STR_LIT>")<EOL>log.info("<STR_LIT>")<EOL>model = Sequential()<EOL>model.add(Dense(<NUM_LIT:8>,<EOL>input_dim=len(res["<STR_LIT>"]),<EOL>kernel_initializer="<STR_LIT>",<EOL>activation="<STR_LIT:relu>"))<EOL>model.add(Dense(<NUM_LIT:6>,<EOL>kernel_initializer="<STR_LIT>",<EOL>activation="<STR_LIT:relu>"))<EOL>model.add(Dense(<NUM_LIT:1>,<EOL>kernel_initializer="<STR_LIT>",<EOL>activation="<STR_LIT>"))<EOL>log.info("<STR_LIT>")<EOL>model.compile(<EOL>loss="<STR_LIT>",<EOL>optimizer="<STR_LIT>",<EOL>metrics=["<STR_LIT>"])<EOL>log.info("<STR_LIT>")<EOL>model.fit(<EOL>res["<STR_LIT>"],<EOL>res["<STR_LIT>"],<EOL>validation_data=(res["<STR_LIT>"],<EOL>res["<STR_LIT>"]),<EOL>epochs=<NUM_LIT:50>,<EOL>batch_size=<NUM_LIT:2>,<EOL>verbose=<NUM_LIT:1>)<EOL>scores = model.evaluate(<EOL>res["<STR_LIT>"],<EOL>res["<STR_LIT>"])<EOL>log.info(("<STR_LIT>")<EOL>.format(<EOL>scores[<NUM_LIT:1>] * <NUM_LIT:100>))<EOL>
build_new_deep_neural_network_from_env_variables

    Build a new deep neural network from environment variables:

    ``CSV_FILE`` - file to process created during prepare dataset
    ``CSV_META_FILE`` - metadata header file created during prepare dataset
    ``PREDICT_FEATURE`` - column to predict
    ``TEST_SIZE`` - split data into percentage of test to training