Merge pull request #1 from Nolte-Group/cleanup

Decoder training modularized

decodertest.py

@@ -7,7 +7,6 @@
 
 import os
 from pathlib import Path
-import time
 
 os.environ["KERAS_BACKEND"] = "jax"
 os.environ["XLA_PYTHON_CLIENT_PREALLOCATE"] = "false"
@@ -27,28 +26,7 @@
 
 from model.model import CompoundModel, DecoderModel
 from pipe.etl import etl, read
-
-with open("parameters.json", "r") as file:
-    params = json.load(file)
-
-    # data parameters
-    SPLIT = params["split"]
-    LOAD_FROM_SCRATCH = params["load_from_scratch"]
-
-    # model parameters
-    HEAD_SIZE = params["head_size"]
-    NUM_HEADS = params["num_heads"]
-    FF_DIM = params["ff_dim"]
-    NUM_TRANSFORMER_BLOCKS = params["num_transformer_blocks"]
-    MLP_UNITS = params["mlp_units"]
-    DROPOUT = params["dropout"]
-    MLP_DROPOUT = params["mlp_dropout"]
-
-    # training parameters
-    BATCH_SIZE = params["batch_size"]
-    EPOCHS = params["epochs"]
-    LOG_LEVEL = params["log_level"]
-    del params
+import train.decoder_train as dt
 
 def trim(dataframe, column):
 
@@ -57,12 +35,6 @@ def trim(dataframe, column):
 
     return ndarray
 
-def get_model(input_shape, n_classes):
-    model = DecoderModel(input_shape, HEAD_SIZE, NUM_HEADS, FF_DIM,
-                 NUM_TRANSFORMER_BLOCKS, MLP_UNITS, n_classes,
-                 dropout=DROPOUT, mlp_dropout=MLP_DROPOUT)
-    return model
-
 def transform(spark, dataframe, keys):
     dataframe = dataframe.withColumn( 
             "index", functions.monotonically_increasing_id()
@@ -127,10 +99,7 @@ def get_data(spark, split=[0.99, 0.005, 0.005]):
 
     return ((trainx, trainy), (valx, valy), (testx, testy), category_dict)
 
-
-def main():
-    # jax mesh setup
-    """
+def multi_gpu():
     devices = jax.devices("gpu")
     mesh = keras.distribution.DeviceMesh(
         shape=(len(devices),), axis_names=["model"], devices=devices
@@ -151,38 +120,33 @@ def main():
             layout_map=layout_map
     )
     keras.distribution.set_distribution(model_parallel)
-    """
+
+def main():
+
+    with open("parameters.json", "r") as file:
+        params = json.load(file)
 
     spark = SparkSession.builder.appName("train").getOrCreate()
 
     keys = ["treatment", "target"]
 
-    LOAD_FROM_SCRATCH = False
-    if LOAD_FROM_SCRATCH:
-        data = etl(spark, split=SPLIT)
+    if params["load_from_scratch"]:
+        (train_set, validation_set,
+         test_set, categories) = etl(spark, split=params["split"])
     else:
-        data = read(spark, split=SPLIT)
+        (train_set, validation_set,
+         test_set, categories) = read(spark, split=params["split"])
 
-    (train_set, validation_set, test_set, categories) = data
 
     n_classes = [dset.shape[1] for dset in train_set[1]]
-    model = get_model(train_set[0].shape[1:], n_classes)
-    model.compile(optimizer=keras.optimizers.Adam(learning_rate=4e-4),
-                  loss=["mse"],
-                  )
-    if True: #LOG_LEVEL == 1:
-        model.summary()
-
-    start = time.time()
-    model.fit(x=train_set[1], y=train_set[0],
-              validation_data=(validation_set[1], validation_set[0]),
-              batch_size=BATCH_SIZE, epochs=EPOCHS, verbose=LOG_LEVEL)
-    end = time.time()
-    print("Training time: ", end - start)
+    decoder = dt.load_decoder(params, train_set[0].shape[1:], n_classes)
 
+    decoder = dt.train_decoder(decoder, params, train_set, validation_set)
     # Test model performance
-    #test_loss, test_accuracy = model.evaluate(test_set[1], test_set[0])
-    test_predict = model.predict(test_set[1])
+
+    metrics = {key: None for key in keys}
+    metrics, test_predict = dt.test_decoder(decoder, test_set, metrics)
+
     plt.pcolor(test_predict[0], cmap='bwr', clim=(-1, 1))
     plt.savefig("sample_spectra.png")
     plt.close()

train/decoder_train.py

@@ -1,42 +1,83 @@
 #-*- coding: utf-8 -*-
 
+import keras
 from keras import Model
+import time
 import typing
 from typing import List
 
-def load_decoder(input_shape, n_classes):
-    """
-    """
+from model.model import DecoderModel
+"""
+# data parameters
+SPLIT = params["split"]
+LOAD_FROM_SCRATCH = params["load_from_scratch"]
 
-    model = DecoderModel(input_shape, HEAD_SIZE, NUM_HEADS, FF_DIM,
-                 NUM_TRANSFORMER_BLOCKS, MLP_UNITS, n_classes,
-                 dropout=DROPOUT, mlp_dropout=MLP_DROPOUT)
-    return model
 
-def train_decoder(decoder, train, validation):
+# training parameters
+BATCH_SIZE = params["batch_size"]
+EPOCHS = params["epochs"]
+LOG_LEVEL = params["log_level"]
+"""
+
+def load_decoder(params, input_shape, n_classes):
     """
     """
 
-    decoder = load_decoder(train_set[0].shape[1:], n_classes)
+    decoder = DecoderModel(
+            input_shape,
+            params["head_size"],
+            params["num_heads"],
+            params["ff_dim"],
+            params["num_transformer_blocks"],
+            params["mlp_units"],
+            n_classes,
+            params["dropout"],
+            params["mlp_dropout"]
+    )
+
     decoder.compile(
         optimizer=keras.optimizers.Adam(learning_rate=4e-4),
-        loss=["mse"],
+        loss=params["loss"],
+        metrics=params["metrics"]
+    )
+
+    return decoder
+
+def train_decoder(decoder, params, train, validation):
+    """
+    """
+
+    start = time.time()
+    decoder.fit(
+        x=train[1],
+        y=train[0],
+        validation_data=(validation[1], validation[0]),
+        batch_size=params["batch_size"],
+        epochs=params["epochs"],
+        verbose=params["log_level"]
     )
+    end = time.time()
+    print("Training time: ", end - start)
+
+    return decoder
 
 def test_decoder(decoder: Model, test: List, metrics: dict):
     """
     """
 
-    test_eval = model.evaluate(test[1], test[0])
+    test_eval = decoder.evaluate(test[1], test[0])
     if len(metrics.keys()) == 1:
         metrics[metrics.keys()[0]] = test_eval
     else:
         for i, key in enumerate(metrics.keys()):
             metrics[key] = test_eval[i]
 
-    test_predict = model.predict(test[1])
+    test_predict = decoder.predict(test[1])
+
     return metrics, test_predict
 
-def save_decoder():
+def save_decoder(decoder: Model):
+
+    return
 
 # EOF