Commit bcc49658 authored by lucas_miranda's avatar lucas_miranda
Browse files

Modified GMVAEP - GRUs instead of LSTMs, stricted clipping, less deep, l1...

Modified GMVAEP - GRUs instead of LSTMs, stricted clipping, less deep, l1 regularization in cluster means, uniform initializer of variances
parent 1ebd5f15
......@@ -15,7 +15,7 @@ import tensorflow_probability as tfp
from tensorflow.keras import Input, Model, Sequential
from tensorflow.keras.activations import softplus
from tensorflow.keras.constraints import UnitNorm
from tensorflow.keras.initializers import he_uniform
from tensorflow.keras.initializers import he_uniform, random_uniform
from tensorflow.keras.layers import BatchNormalization, Bidirectional
from tensorflow.keras.layers import Dense, Dropout, GRU
from tensorflow.keras.layers import RepeatVector, Reshape, TimeDistributed
......@@ -155,7 +155,7 @@ class GMVAE:
Model_E0 = tf.keras.layers.Conv1D(
filters=self.CONV_filters,
kernel_size=5,
strides=2, # Increased strides to yield shorter sequences
strides=2, # Increased strides to yield shorter sequences
padding="same",
activation=self.dense_activation,
kernel_initializer=he_uniform(),
......@@ -398,6 +398,7 @@ class GMVAE:
// 2,
name="cluster_means",
activation=None,
activity_regularizer=(tf.keras.regularizers.l1(10e-5)),
kernel_initializer=he_uniform(),
)(encoder)
......@@ -411,6 +412,7 @@ class GMVAE:
activity_regularizer=(
tf.keras.regularizers.l2(0.01) if self.reg_cluster_variance else None
),
kernel_initializer=random_uniform(),
)(encoder)
z_gauss = tf.keras.layers.concatenate([z_gauss_mean, z_gauss_var], axis=1)
......@@ -638,4 +640,4 @@ class GMVAE:
# - Think about using spectral normalization
# - REVISIT DROPOUT - CAN HELP WITH TRAINING STABILIZATION
# - Decrease learning rate!
# - Implement residual blocks!
\ No newline at end of file
# - Implement residual blocks!
......@@ -52,11 +52,11 @@ def load_treatments(train_path):
to be loaded as metadata in the coordinates class"""
try:
with open(
os.path.join(
train_path,
[i for i in os.listdir(train_path) if i.endswith(".json")][0],
),
"r",
os.path.join(
train_path,
[i for i in os.listdir(train_path) if i.endswith(".json")][0],
),
"r",
) as handle:
treatment_dict = json.load(handle)
except IndexError:
......@@ -66,25 +66,25 @@ def load_treatments(train_path):
def get_callbacks(
X_train: np.array,
batch_size: int,
phenotype_prediction: float,
next_sequence_prediction: float,
rule_based_prediction: float,
overlap_loss: float,
loss: str,
loss_warmup: int = 0,
warmup_mode: str = "none",
X_val: np.array = None,
input_type: str = False,
cp: bool = False,
reg_cat_clusters: bool = False,
reg_cluster_variance: bool = False,
entropy_samples: int = 15000,
entropy_knn: int = 100,
logparam: dict = None,
outpath: str = ".",
run: int = False,
X_train: np.array,
batch_size: int,
phenotype_prediction: float,
next_sequence_prediction: float,
rule_based_prediction: float,
overlap_loss: float,
loss: str,
loss_warmup: int = 0,
warmup_mode: str = "none",
X_val: np.array = None,
input_type: str = False,
cp: bool = False,
reg_cat_clusters: bool = False,
reg_cluster_variance: bool = False,
entropy_samples: int = 15000,
entropy_knn: int = 100,
logparam: dict = None,
outpath: str = ".",
run: int = False,
) -> List[Union[Any]]:
"""Generates callbacks for model training, including:
- run_ID: run name, with coarse parameter details;
......@@ -202,15 +202,15 @@ def log_hyperparameters(phenotype_class: float, rec: str):
# noinspection PyUnboundLocalVariable
def tensorboard_metric_logging(
run_dir: str,
hpms: Any,
ae: Any,
X_val: np.ndarray,
y_val: np.ndarray,
next_sequence_prediction: float,
phenotype_prediction: float,
rule_based_prediction: float,
rec: str,
run_dir: str,
hpms: Any,
ae: Any,
X_val: np.ndarray,
y_val: np.ndarray,
next_sequence_prediction: float,
phenotype_prediction: float,
rule_based_prediction: float,
rec: str,
):
"""Autoencoder metric logging in tensorboard"""
......@@ -270,35 +270,35 @@ def tensorboard_metric_logging(
def autoencoder_fitting(
preprocessed_object: Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray],
batch_size: int,
encoding_size: int,
epochs: int,
hparams: dict,
kl_annealing_mode: str,
kl_warmup: int,
log_history: bool,
log_hparams: bool,
loss: str,
mmd_annealing_mode: str,
mmd_warmup: int,
montecarlo_kl: int,
n_components: int,
output_path: str,
overlap_loss: float,
next_sequence_prediction: float,
phenotype_prediction: float,
rule_based_prediction: float,
pretrained: str,
save_checkpoints: bool,
save_weights: bool,
reg_cat_clusters: bool,
reg_cluster_variance: bool,
entropy_samples: int,
entropy_knn: int,
input_type: str,
run: int = 0,
strategy: tf.distribute.Strategy = tf.distribute.MirroredStrategy(),
preprocessed_object: Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray],
batch_size: int,
encoding_size: int,
epochs: int,
hparams: dict,
kl_annealing_mode: str,
kl_warmup: int,
log_history: bool,
log_hparams: bool,
loss: str,
mmd_annealing_mode: str,
mmd_warmup: int,
montecarlo_kl: int,
n_components: int,
output_path: str,
overlap_loss: float,
next_sequence_prediction: float,
phenotype_prediction: float,
rule_based_prediction: float,
pretrained: str,
save_checkpoints: bool,
save_weights: bool,
reg_cat_clusters: bool,
reg_cluster_variance: bool,
entropy_samples: int,
entropy_knn: int,
input_type: str,
run: int = 0,
strategy: tf.distribute.Strategy = tf.distribute.MirroredStrategy(),
):
"""Implementation function for deepof.data.coordinates.deep_unsupervised_embedding"""
......@@ -317,8 +317,8 @@ def autoencoder_fitting(
# Generate validation dataset for callback usage
X_val_dataset = (
tf.data.Dataset.from_tensor_slices(X_val)
.with_options(options)
.batch(batch_size * strategy.num_replicas_in_sync, drop_remainder=True)
.with_options(options)
.batch(batch_size * strategy.num_replicas_in_sync, drop_remainder=True)
)
# Defines what to log on tensorboard (useful for trying out different models)
......@@ -361,7 +361,7 @@ def autoencoder_fitting(
logparams, metrics = log_hyperparameters(phenotype_prediction, rec)
with tf.summary.create_file_writer(
os.path.join(output_path, "hparams", run_ID)
os.path.join(output_path, "hparams", run_ID)
).as_default():
hp.hparams_config(
hparams=logparams,
......@@ -422,28 +422,28 @@ def autoencoder_fitting(
Xvals, yvals = X_val[:-1], [X_val[:-1], X_val[1:]]
if phenotype_prediction > 0.0:
ys += [y_train[-Xs.shape[0]:, 0]]
yvals += [y_val[-Xvals.shape[0]:, 0]]
ys += [y_train[-Xs.shape[0] :, 0]]
yvals += [y_val[-Xvals.shape[0] :, 0]]
# Remove the used column (phenotype) from both y arrays
y_train = y_train[:, 1:]
y_val = y_val[:, 1:]
if rule_based_prediction > 0.0:
ys += [y_train[-Xs.shape[0]:]]
yvals += [y_val[-Xvals.shape[0]:]]
ys += [y_train[-Xs.shape[0] :]]
yvals += [y_val[-Xvals.shape[0] :]]
# Convert data to tf.data.Dataset objects
train_dataset = (
tf.data.Dataset.from_tensor_slices((Xs, tuple(ys)))
.batch(batch_size * strategy.num_replicas_in_sync, drop_remainder=True)
.shuffle(buffer_size=X_train.shape[0])
.with_options(options)
.batch(batch_size * strategy.num_replicas_in_sync, drop_remainder=True)
.shuffle(buffer_size=X_train.shape[0])
.with_options(options)
)
val_dataset = (
tf.data.Dataset.from_tensor_slices((Xvals, tuple(yvals)))
.batch(batch_size * strategy.num_replicas_in_sync, drop_remainder=True)
.with_options(options)
.batch(batch_size * strategy.num_replicas_in_sync, drop_remainder=True)
.with_options(options)
)
ae.fit(
......@@ -484,23 +484,23 @@ def autoencoder_fitting(
def tune_search(
data: List[np.array],
encoding_size: int,
hypertun_trials: int,
hpt_type: str,
k: int,
kl_warmup_epochs: int,
loss: str,
mmd_warmup_epochs: int,
overlap_loss: float,
next_sequence_prediction: float,
phenotype_prediction: float,
rule_based_prediction: float,
project_name: str,
callbacks: List,
n_epochs: int = 30,
n_replicas: int = 1,
outpath: str = ".",
data: List[np.array],
encoding_size: int,
hypertun_trials: int,
hpt_type: str,
k: int,
kl_warmup_epochs: int,
loss: str,
mmd_warmup_epochs: int,
overlap_loss: float,
next_sequence_prediction: float,
phenotype_prediction: float,
rule_based_prediction: float,
project_name: str,
callbacks: List,
n_epochs: int = 30,
n_replicas: int = 1,
outpath: str = ".",
) -> Union[bool, Tuple[Any, Any]]:
"""Define the search space using keras-tuner and bayesian optimization
......@@ -592,16 +592,16 @@ def tune_search(
Xvals, yvals = X_val[:-1], [X_val[:-1], X_val[1:]]
if phenotype_prediction > 0.0:
ys += [y_train[-Xs.shape[0]:, 0]]
yvals += [y_val[-Xvals.shape[0]:, 0]]
ys += [y_train[-Xs.shape[0] :, 0]]
yvals += [y_val[-Xvals.shape[0] :, 0]]
# Remove the used column (phenotype) from both y arrays
y_train = y_train[:, 1:]
y_val = y_val[:, 1:]
if rule_based_prediction > 0.0:
ys += [y_train[-Xs.shape[0]:]]
yvals += [y_val[-Xvals.shape[0]:]]
ys += [y_train[-Xs.shape[0] :]]
yvals += [y_val[-Xvals.shape[0] :]]
tuner.search(
Xs,
......
......@@ -2,9 +2,18 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 36,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n"
]
}
],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
......@@ -54,7 +63,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
......@@ -67,6 +76,7 @@
"from collections import Counter\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"from datetime import datetime\n",
"from sklearn.manifold import TSNE\n",
"from sklearn.decomposition import PCA\n",
"from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
......@@ -615,7 +625,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
......@@ -630,7 +640,7 @@
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
......@@ -647,7 +657,7 @@
" compile_model=True,\n",
" batch_size=batch_size,\n",
" encoding=encoding,\n",
" next_sequence_prediction=0.1,\n",
" next_sequence_prediction=NextSeqPred,\n",
" phenotype_prediction=PhenoPred,\n",
" rule_based_prediction=RuleBasedPred,\n",
").build(\n",
......@@ -658,7 +668,7 @@
},
{
"cell_type": "code",
"execution_count": 27,
"execution_count": 38,
"metadata": {
"scrolled": false
},
......@@ -671,84 +681,51 @@
"__________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
"input_15 (InputLayer) [(None, 22, 26)] 0 \n",
"input_19 (InputLayer) [(None, 22, 26)] 0 \n",
"__________________________________________________________________________________________________\n",
"conv1d_24 (Conv1D) (None, 11, 64) 8384 input_15[0][0] \n",
"conv1d_33 (Conv1D) (None, 11, 64) 8384 input_19[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_94 (BatchNo (None, 11, 64) 256 conv1d_24[0][0] \n",
"batch_normalization_118 (BatchN (None, 11, 64) 256 conv1d_33[0][0] \n",
"__________________________________________________________________________________________________\n",
"bidirectional_48 (Bidirectional (None, 11, 256) 148992 batch_normalization_94[0][0] \n",
"bidirectional_60 (Bidirectional (None, 11, 256) 197632 batch_normalization_118[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_95 (BatchNo (None, 11, 256) 1024 bidirectional_48[0][0] \n",
"batch_normalization_119 (BatchN (None, 11, 256) 1024 bidirectional_60[0][0] \n",
"__________________________________________________________________________________________________\n",
"bidirectional_49 (Bidirectional (None, 128) 123648 batch_normalization_95[0][0] \n",
"bidirectional_61 (Bidirectional (None, 128) 164352 batch_normalization_119[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_96 (BatchNo (None, 128) 512 bidirectional_49[0][0] \n",
"batch_normalization_120 (BatchN (None, 128) 512 bidirectional_61[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_88 (Dense) (None, 64) 8256 batch_normalization_96[0][0] \n",
"dense_109 (Dense) (None, 64) 8256 batch_normalization_120[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_97 (BatchNo (None, 64) 256 dense_88[0][0] \n",
"batch_normalization_121 (BatchN (None, 64) 256 dense_109[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_8 (Dropout) (None, 64) 0 batch_normalization_97[0][0] \n",
"dropout_10 (Dropout) (None, 64) 0 batch_normalization_121[0][0] \n",
"__________________________________________________________________________________________________\n",
"sequential_14 (Sequential) (None, 32) 2208 dropout_8[0][0] \n",
"sequential_18 (Sequential) (None, 32) 2208 dropout_10[0][0] \n",
"__________________________________________________________________________________________________\n",
"cluster_means (Dense) (None, 90) 2970 sequential_14[0][0] \n",
"cluster_means (Dense) (None, 90) 2970 sequential_18[0][0] \n",
"__________________________________________________________________________________________________\n",
"cluster_variances (Dense) (None, 90) 2970 sequential_14[0][0] \n",
"cluster_variances (Dense) (None, 90) 2970 sequential_18[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_14 (Concatenate) (None, 180) 0 cluster_means[0][0] \n",
"concatenate_19 (Concatenate) (None, 180) 0 cluster_means[0][0] \n",
" cluster_variances[0][0] \n",
"__________________________________________________________________________________________________\n",
"cluster_assignment (Dense) (None, 15) 495 sequential_14[0][0] \n",
"cluster_assignment (Dense) (None, 15) 495 sequential_18[0][0] \n",
"__________________________________________________________________________________________________\n",
"reshape_8 (Reshape) (None, 12, 15) 0 concatenate_14[0][0] \n",
"reshape_10 (Reshape) (None, 12, 15) 0 concatenate_19[0][0] \n",
"__________________________________________________________________________________________________\n",
"encoding_distribution (Distribu multiple 0 cluster_assignment[0][0] \n",
" reshape_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"kl_divergence_layer_6 (KLDiverg multiple 181 encoding_distribution[0][0] \n",
"__________________________________________________________________________________________________\n",
"latent_distribution (Lambda) multiple 0 kl_divergence_layer_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_97 (Dense) (None, 32) 224 latent_distribution[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_102 (BatchN (None, 32) 128 dense_97[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_92 (Dense) (None, 64) 2112 batch_normalization_102[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_103 (BatchN (None, 64) 256 dense_92[0][0] \n",
"__________________________________________________________________________________________________\n",
"repeat_vector_9 (RepeatVector) (None, 22, 64) 0 batch_normalization_103[0][0] \n",
"__________________________________________________________________________________________________\n",
"bidirectional_52 (Bidirectional (None, 22, 256) 148992 repeat_vector_9[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_104 (BatchN (None, 22, 256) 1024 bidirectional_52[0][0] \n",
"__________________________________________________________________________________________________\n",
"bidirectional_53 (Bidirectional (None, 22, 256) 296448 batch_normalization_104[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_105 (BatchN (None, 22, 256) 1024 bidirectional_53[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv1d_26 (Conv1D) (None, 22, 64) 81984 batch_normalization_105[0][0] \n",
" reshape_10[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_99 (Dense) (None, 22, 26) 1690 conv1d_26[0][0] \n",
"kl_divergence_layer_8 (KLDiverg multiple 181 encoding_distribution[0][0] \n",
"__________________________________________________________________________________________________\n",
"tf.math.softplus_7 (TFOpLambda) (None, 22, 26) 0 dense_99[0][0] \n",
"latent_distribution (Lambda) multiple 0 kl_divergence_layer_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_98 (Dense) (None, 22, 26) 1690 conv1d_26[0][0] \n",
"__________________________________________________________________________________________________\n",
"lambda_7 (Lambda) (None, 22, 26) 0 tf.math.softplus_7[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_16 (Concatenate) (None, 22, 52) 0 dense_98[0][0] \n",
" lambda_7[0][0] \n",
"__________________________________________________________________________________________________\n",
"vae_reconstruction (Functional) multiple 337940 latent_distribution[0][0] \n",
"__________________________________________________________________________________________________\n",
"vae_prediction (IndependentNorm multiple 0 concatenate_16[0][0] \n",
"vae_reconstruction (Functional) multiple 419092 latent_distribution[0][0] \n",
"==================================================================================================\n",
"Total params: 1,173,664\n",
"Trainable params: 1,170,271\n",
"Non-trainable params: 3,393\n",
"Total params: 808,588\n",
"Trainable params: 806,411\n",
"Non-trainable params: 2,177\n",
"__________________________________________________________________________________________________\n"
]
}
......@@ -763,7 +740,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
......@@ -787,7 +764,7 @@
"# plot_model(encoder, \"encoder\")\n",
"# plot_model(decoder, \"decoder\")\n",
"# plot_model(grouper, \"grouper\")\n",
"# plot_model(gmvaep, \"gmvaep\")"
"plot_model(gmvaep, \"gmvaep\")"
]
},
{
......@@ -799,7 +776,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
......@@ -812,7 +789,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
......@@ -840,7 +817,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
......@@ -865,21 +842,22 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 42,
"metadata": {
"scrolled": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'tf' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-8a0b102aafa0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m initializers = [\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minitializers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mConstant\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minitializers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGlorotNormal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minitializers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGlorotUniform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'tf' is not defined"
]
"data": {
"image/png": "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