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train_imdb.py
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train_imdb.py
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# Copyright 2021 The ODE-LSTM Authors. All Rights Reserved.
import os
import subprocess
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
import tensorflow as tf
import numpy as np
import argparse
from tf_cfc import CfcCell, MixedCfcCell, LTCCell
import sys
vocab_size = 20000 # Only consider the top 20k words
maxlen = 200 # Only consider the first 200 words of each movie review
def load_imdb():
"""
## Download and prepare dataset
"""
(x_train, y_train), (x_val, y_val) = tf.keras.datasets.imdb.load_data(
num_words=vocab_size
)
x_train = tf.keras.preprocessing.sequence.pad_sequences(x_train, maxlen=maxlen)
x_val = tf.keras.preprocessing.sequence.pad_sequences(x_val, maxlen=maxlen)
return (x_train, y_train), (x_val, y_val)
def eval(config, index_arg, verbose=0):
(train_x, train_y), (test_x, test_y) = load_imdb()
if config["use_mixed"]:
cell = MixedCfcCell(units=config["size"], hparams=config)
else:
cell = CfcCell(units=config["size"], hparams=config)
# pixel_input = tf.keras.Input(shape=(28 * 28, 1), name="pixel")
inputs = tf.keras.layers.Input(shape=(maxlen,))
token_emb = tf.keras.layers.Embedding(
input_dim=vocab_size, output_dim=config["embed_dim"]
)
cell_input = token_emb(inputs)
cell_input = tf.keras.layers.Dropout(config["embed_dr"])(cell_input)
rnn = tf.keras.layers.RNN(cell, time_major=False, return_sequences=False)
dense_layer = tf.keras.layers.Dense(10)
output_states = rnn(cell_input)
y = dense_layer(output_states)
model = tf.keras.Model(inputs, y)
base_lr = config["base_lr"]
decay_lr = config["decay_lr"]
# end_lr = config["end_lr"]
train_steps = train_x.shape[0] // config["batch_size"]
learning_rate_fn = tf.keras.optimizers.schedules.ExponentialDecay(
base_lr, train_steps, decay_lr
)
opt = (
tf.keras.optimizers.Adam
if config["optimizer"] == "adam"
else tf.keras.optimizers.RMSprop
)
optimizer = opt(learning_rate_fn, clipnorm=config["clipnorm"])
model.compile(
optimizer=optimizer,
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],
)
# Fit and evaluate
hist = model.fit(
x=train_x,
y=train_y,
batch_size=config["batch_size"],
epochs=config["epochs"],
validation_data=(test_x, test_y) if verbose else None,
verbose=verbose,
)
# test_accuracies = hist.history["val_sparse_categorical_accuracy"]
# return np.max(test_accuracies)
_, test_accuracy = model.evaluate(test_x, test_y, verbose=0)
return test_accuracy
BEST_MIXED = {
"clipnorm": 10,
"optimizer": "rmsprop",
"batch_size": 128,
"size": 64,
"embed_dim": 32,
"embed_dr": 0.3,
"epochs": 20,
"base_lr": 0.0005,
"decay_lr": 0.8,
"backbone_activation": "lecun",
"backbone_dr": 0.0,
"backbone_units": 64,
"backbone_layers": 1,
"weight_decay": 0.00029,
"use_mixed": True,
}
# 87.04% (MAX)
# 85.91% $\pm$ 0.99
BEST_DEFAULT = {
"clipnorm": 10,
"optimizer": "rmsprop",
"batch_size": 128,
"size": 192,
"embed_dim": 192,
"embed_dr": 0.0,
"epochs": 47,
"base_lr": 0.0005,
"decay_lr": 0.7,
"backbone_activation": "silu",
"backbone_dr": 0.0,
"backbone_units": 64,
"backbone_layers": 2,
"weight_decay": 3.6e-05,
"use_mixed": False,
"no_gate": False,
}
# 87.52\% $\pm$ 0.09
BEST_NO_GATE = {
"clipnorm": 5,
"optimizer": "rmsprop",
"batch_size": 128,
"size": 224,
"embed_dim": 192,
"embed_dr": 0.2,
"epochs": 37,
"base_lr": 0.0005,
"decay_lr": 0.8,
"backbone_activation": "silu",
"backbone_dr": 0.1,
"backbone_units": 128,
"backbone_layers": 1,
"weight_decay": 2.7e-05,
"use_mixed": False,
"no_gate": True,
"minimal": False,
}
# 81.72\% $\pm$ 0.50
BEST_MINIMAL = {
"clipnorm": 1,
"optimizer": "adam",
"batch_size": 128,
"size": 320,
"embed_dim": 64,
"embed_dr": 0.0,
"epochs": 27,
"base_lr": 0.0005,
"decay_lr": 0.8,
"backbone_activation": "relu",
"backbone_dr": 0.0,
"backbone_units": 64,
"backbone_layers": 1,
"weight_decay": 0.00048,
"use_mixed": False,
"no_gate": False,
"minimal": True,
}
# 61.76\% $\pm$ 6.14
BEST_LTC = {
"clipnorm": 10,
"optimizer": "adam",
"batch_size": 128,
"size": 128,
"embed_dim": 64,
"embed_dr": 0.0,
"epochs": 50,
"base_lr": 0.05,
"decay_lr": 0.95,
"backbone_activation": "lecun",
"backbone_dr": 0.0,
"forget_bias": 2.4,
"backbone_units": 128,
"backbone_layers": 1,
"weight_decay": 1e-05,
"use_mixed": False,
"no_gate": False,
"minimal": False,
"use_ltc": True,
}
def score(config):
acc = []
for i in range(5):
acc.append(100 * eval(config, i))
print(
f"IMDB test accuracy [{len(acc)}/5]: {np.mean(acc):0.2f}\\% $\\pm$ {np.std(acc):0.2f}"
)
print(f"IMDB test accuracy: {np.mean(acc):0.2f}\\% $\\pm$ {np.std(acc):0.2f}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--use_mixed", action="store_true")
parser.add_argument("--no_gate", action="store_true")
parser.add_argument("--minimal", action="store_true")
parser.add_argument("--use_ltc", action="store_true")
args = parser.parse_args()
if args.minimal:
score(BEST_MINIMAL)
elif args.no_gate:
score(BEST_NO_GATE)
elif args.use_ltc:
score(LTC_TEST)
elif args.use_mixed:
score(BEST_MIXED)
else:
score(BEST_DEFAULT)