Keras predicting floating point output for a binary problem - python

I have a model in Keras:
import tensorflow as tf
import numpy as np
import pandas as pd
from tensorflow import keras
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split
import random
df = pd.read_csv('/home/Datasets/creditcard.csv')
output = df['Class']
features = df.drop('Class', 1)
train_features, test_features, train_labels, test_labels = train_test_split(df, output, test_size = 0.2, random_state = 42)
train_features = train_features.to_numpy()
test_features = test_features.to_numpy()
train_labels = train_labels.to_numpy()
test_labels = test_labels.to_numpy()
model = tf.keras.Sequential()
num_nodes = [1]
act_functions = [tf.nn.relu]
optimizers = ['SGD']
loss_functions = ['categorical_crossentropy']
epochs_count = ['10']
batch_sizes = ['500']
act = random.choice(act_functions)
opt = random.choice(optimizers)
ep = random.choice(epochs_count)
batch = random.choice(batch_sizes)
loss = random.choice(loss_functions)
count = random.choice(num_nodes)
model.add(tf.keras.layers.Dense(31, activation = act, input_shape=(31,)))
model.add(tf.keras.layers.Dense(count, activation = act))
model.add(tf.keras.layers.Dense(1, activation = act))
model.compile(loss = loss,
optimizer = opt,
metrics = ['accuracy'])
epochs = int(ep)
batch_size = int(batch)
model.fit(train_features, train_labels, epochs=epochs, batch_size=batch_size)
The train labels are binary:
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
But, the output of:
z = model.predict(test_features)
is:
array([[ 4574.6 ],
[ 4896.158 ],
[ 3867.8225],
...,
[15516.117 ],
[ 6441.43 ],
[ 5453.437 ]], dtype=float32)
Why would it be predicting these values?
Thanks


On your last layer use sigmoid activation,
model.add(tf.keras.layers.Dense(1, activation='sigmoid'))

Related

RuntimeError: Could not infer dtype of dict

RuntimeError: Could not infer dtype of dict
I'm getting above error when i'm using a pre-trained transformer model from hugging face
https://huggingface.co/SEBIS/code_trans_t5_base_code_comment_generation_java_transfer_learning_finetune
I want to fine-tune the model on one of the custom datasets that I have prepared.
Please access the dataset from Kaggle
https://www.kaggle.com/datasets/codkiller0911/kotlin-code-snippets
adding the dataset tot he notebook
import pandas as pd
train_dataset = pd.read_csv('kotlin_code_comment_train.tsv',sep = '\t')
val_dataset = pd.read_csv('kotlin_code_comment_test.tsv',sep = '\t')
Preparing the dataset for tokenisation
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from transformers import TrainingArguments, Trainer
tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_code_comment_generation_java_transfer_learning_finetune")
model = AutoModelForSeq2SeqLM.from_pretrained("SEBIS/code_trans_t5_base_code_comment_generation_java_transfer_learning_finetune")
train_encodings = tokenizer(train_dataset["Code_Function"].values.tolist(),truncation=True,padding=True,max_length=512)
train_message = tokenizer(train_dataset["Message"].values.tolist(),truncation=True,padding=True,max_length=512)
val_encodings = tokenizer(val_dataset["code_function"].values.tolist(),
truncation=True,
padding=True)
val_message = tokenizer(val_dataset["message"].values.tolist(),
truncation=True,
padding=True)
Loading the model and training args to start the training
import torch
from torch.utils.data import Dataset, DataLoader
class CustomTextDataset(Dataset):
def __init__(self, txt, labels):
self.labels = labels
self.text = txt
def __getitem__(self, idx):
text = {key: torch.tensor(val[idx]) for key, val in self.text.items()}
if self.labels:
text["labels"] = {key: torch.tensor(val[idx]) for key, val in self.labels.items()}
return text
def __len__(self):
return len(self.labels["input_ids"])
train_dataset = CustomTextDataset(train_encodings, train_message)
val_dataset = CustomTextDataset(val_encodings, val_message)
def compute_metrics(p):
print(type(p))
pred, labels = p
pred = np.argmax(pred, axis=1)
accuracy = accuracy_score(y_true=labels, y_pred=pred)
recall = recall_score(y_true=labels, y_pred=pred)
precision = precision_score(y_true=labels, y_pred=pred)
f1 = f1_score(y_true=labels, y_pred=pred)
return {"accuracy": accuracy, "precision": precision, "recall": recall, "f1": f1}
# Define Trainer
args = TrainingArguments(
output_dir="output",
max_steps=3000
)
trainer = Trainer(
model=model,
args=args,
train_dataset=train_dataset,
eval_dataset=val_dataset,
compute_metrics=compute_metrics
)
trainer.train()
After tokenization this is how the dataset looks tokenisation
{'input_ids': tensor([ 3800, 9146, 259, 588, 542, 219, 6698, 171, 9146, 259,
588, 542, 402, 12, 312, 587, 745, 14, 3199, 1330,
11, 204, 35, 540, 587, 745, 14, 3199, 1330, 29,
5, 6698, 35, 587, 554, 1233, 15571, 12, 32, 35,
3963, 11, 8338, 542, 1233, 219, 6698, 35, 3963, 402,
16, 32, 35, 1838, 11, 204, 35, 1838, 16, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0]),
'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0]),
'labels': {'input_ids': tensor([9401, 10, 15, 2406, 755, 41, 19, 60, 42, 204, 19, 113,
236, 42, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0]),
'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0])}}
When I execute the training command I get the above-mentioned error.
Anyone have an solutions to this. Kind of banging my head on a wall for quite some time now in finding an solution to this.

What model to use to predict rare occurances? For example, when someone takes medicine

I've tried to build a prediction model that would give me a probability of a person taking medicine given certain conditions. What I'm most interested about is that the model would relatively accurately be able to predict when someone takes medicine. I have a dataframe that has 1400 rows, where about 134 rows are those where the user takes medicine. I have a df that looks somewhat like the example below.
df = pd.DataFrame({'time_hour': ['6', '12', '18'],
'weekday': [6, 1, 3],
'previous_action': ['eat', 'sleep', 'eat'],
'take_medicine': [0, 1, 1]})
I've tried solving this with logistic regression and bernoulli naive bayes, but each of them only bet on the most common outcome, which is the person not taking medicine. I've tried googling how to solve this without success.
I've looked at the data and the person takes medicine daily at 12 and 18, so I'm curious why the results are so bad. Is there another model that would suit this kind of problem better or should I be doing something differently?
Here is an example what I've done previously
predictors = ['time_hour', 'weekday', 'previous_action']
X = df[predictors]
y = df['take_medicine']
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.25,random_state=0)
from sklearn.naive_bayes import BernoulliNB
bert = BernoulliNB()
bert.fit(X_train, y_train)
y_pred = bert.predict(X_test)
y_pred
Which returns
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

Predicting rare occurrences is called anomaly detection, you can look it up, there are many models that might be useful in your case such as isolation forest that is present in the sklearn library.

Deep Learning Model to Predict Clicks from Keywords

I have a dataset of keywords and clicks.
I'm trying to build a model where it takes in a phrase of keyword ( not more than 5 words, eg: mechanical engineer ) and outputs a value (like clicks, eg: 56). I'm using the bag of words approach which resulted in about 40% accuracy which is not good enough. Can I get some opinions on what approach you would take to improve the accuracy?
Or perhaps my approach is wrong ?
After cleaning,
Here's my code:
words = []
for row in df['Keyword']:
row = nltk.word_tokenize(row)
for i in row:
words.append(i)
words = sorted(list(set(words)))
training = []
for x in df['Keyword']:
bag = []
wrds = nltk.word_tokenize(x)
for w in words:
if w in wrds:
bag.append(1)
else:
bag.append(0)
training.append(bag)
model = keras.Sequential()
inputs = keras.Input(shape=(858,))
x = layers.Embedding(858, 8, input_length=5)(inputs)
x = layers.Flatten()(x)
outputs = layers.Dense(1, activation='relu')(x)
model = keras.Model(inputs=inputs, outputs=outputs, name='my_model')
model.compile(optimizer='adam',loss='mean_squared_error',metrics=['accuracy'])
history = model.fit(X_train, Y_train,
batch_size=50,
epochs=20,
validation_split=0.2,
verbose = 1)
Here's a sample output of my X_train and Y_train.
X_train:
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
Y_train:
257.43
I have about 330k samples.
Any input in appreciated. Thanks

This seems to be a regression problem, not a classification problem.
What does the accuracy of 40% tell you? It tells you that in 40% of the test cases, the network did predict the exact number of clicks, in all the other cases, it did not, even if the number of clicks it predicted is around the right number of clicks. Have a look at this question.
Instead, you should use the error as metric. It tells you how exactly your model can predict the number of clicks, whereas accuracy tells you how often your model predicted the exact number of clicks.
With this in mind, an accuracy of 40% seems pretty high. If you insist of accuracy as a metric, have a look at this.

How to build input to predict with saved model for BERT SQuAD2.0 with tensorflow

I am trying to build the input for the saved model from BERT-SQuAD given that I have got all the elements for the input.
I fine-tuned a question answering model by running of run_squad.py in Google bert, then I exported the model with export_saved_model. Now when I have a new context and question, I can't build the correct input that can get return output from the model.
Code to export the model:
#export the model
def serving_input_receiver_fn():
feature_spec = {
"unique_ids": tf.FixedLenFeature([], tf.int64),
"input_ids": tf.FixedLenFeature([FLAGS.max_seq_length], tf.int64),
"input_mask": tf.FixedLenFeature([FLAGS.max_seq_length], tf.int64),
"segment_ids": tf.FixedLenFeature([FLAGS.max_seq_length], tf.int64),
}
serialized_tf_example = tf.placeholder(dtype=tf.string,
shape=FLAGS.predict_batch_size,
name='input_example_tensor')
receiver_tensors = {'examples': serialized_tf_example}
features = tf.parse_example(serialized_tf_example, feature_spec)
return tf.estimator.export.ServingInputReceiver(features, receiver_tensors)
estimator = tf.contrib.tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
predict_batch_size=FLAGS.predict_batch_size)
estimator._export_to_tpu = False ## !!important to add this
estimator.export_saved_model(
export_dir_base ="C:/Users/ZitongZhou/Desktop/qa/bert_squad/servemodel",
serving_input_receiver_fn = serving_input_receiver_fn)
The way I loaded the model:
export_dir = 'servemodel'
subdirs = [x for x in Path(export_dir).iterdir()
if x.is_dir() and 'temp' not in str(x)]
latest = str(sorted(subdirs)[-1])
predict_fn = predictor.from_saved_model(latest)
I got the eval_features from the run_squad.py.
The way I tried to build the input:
feature_spec = {
"unique_ids": np.asarray(eval_features[0].unique_id).tolist(),
"input_ids": np.asarray(eval_features[0].input_ids).tolist(),
"input_mask": np.asarray(eval_features[0].input_mask).tolist(),
"segment_ids": np.asarray(eval_features[0].segment_ids).tolist()
}
serialized_tf_example = tf.placeholder(dtype=tf.string,
shape=[1],
name='input_example_tensor')
receiver_tensors = {'examples': serialized_tf_example}
features = tf.parse_example(serialized_tf_example, feature_spec)
out = predict_fn({'examples':[str(feature_spec)]})
I expect to get a prediction 'out' so I can extract the answer to the question from it.
The traceback I got:
Traceback (most recent call last):
File "<ipython-input-51-0c3b618a8f48>", line 11, in <module>
features = tf.parse_example(serialized_tf_example, feature_spec)
File "C:\Users\ZitongZhou\Anaconda3\envs\nlp\lib\site-packages\tensorflow
\python\ops\parsing_ops.py", line 580, in parse_example
return parse_example_v2(serialized, features, example_names, name)
File "C:\Users\ZitongZhou\Anaconda3\envs\nlp\lib\site-packages\tensorflow
\python\ops\parsing_ops.py", line 803, in parse_example_v2
[VarLenFeature, SparseFeature, FixedLenFeature, FixedLenSequenceFeature])
File "C:\Users\ZitongZhou\Anaconda3\envs\nlp\lib\site-packages\tensorflow
\python\ops\parsing_ops.py", line 299, in _features_to_raw_params
raise ValueError("Invalid feature %s:%s." % (key, feature))
ValueError: Invalid feature input_ids:[101, 1005, 2129, 2214, 2003, 19523,
6562, 1005, 102, 1005, 19523, 11233, 2003, 2274, 2086, 2214, 1005, 102,
0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0].

I figured it out, I need to use tf.train.Example function:
def create_int_feature(values):
f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
return f
inputs = collections.OrderedDict()
inputs["input_ids"] = create_int_feature(features[0].input_ids)
inputs["input_mask"] = create_int_feature(features[0].input_mask)
inputs["segment_ids"] = create_int_feature(features[0].segment_ids)
inputs["unique_ids"] = create_int_feature([features[0].unique_id])
tf_example = tf.train.Example(features=tf.train.Features(feature=inputs))
out = predict_fn({'examples':[tf_example.SerializeToString()]})

LSTM, multiple binary array input and overfitting handling

Now I'm working on a space environment model that predicts the maximum Kp index of tomorrow using last 3-days coronal hole information. (Total amount of data is around 4300 days.)
For the input, 3 arrays with 136 elements are used (one array for a day, so 3 days data). For example,
inputArray_day1 = [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
inputArray_day2 = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0]
inputArray_day3 = [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
The output is single one-hot vector of length 28 which indicates maximum Kp index of day4. I use dictionaries below to convert between Kp index and one-hot vector easily.
kp2idx = {0.0:0, 0.3:1, 0.7:2, 1.0:3, 1.3:4, 1.7:5, 2.0:6, 2.3:7, 2.7:8, 3.0:9, 3.3:10, 3.7:11, 4.0:12, 4.3:13,
4.7:14, 5.0:15, 5.3:16, 5.7:17, 6.0:18, 6.3:19, 6.7:20, 7.0:21, 7.3:22, 7.7:23, 8.0:24, 8.3:25, 8.7:26, 9.0:27}
idx2kp = {0:0.0, 1:0.3, 2:0.7, 3:1.0, 4:1.3, 5:1.7, 6:2.0, 7:2.3, 8:2.7, 9:3.0, 10:3.3, 11:3.7, 12:4.0, 13:4.3,
14:4.7, 15:5.0, 16:5.3, 17:5.7, 18:6.0, 19:6.3, 20:6.7, 21:7.0, 22:7.3, 23:7.7, 24:8.0, 25:8.3, 26:8.7, 27:9.0}
The model contains two LSTM layers with dropout.
def fit_lstm2(X,Y,Xv,Yv, n_batch, nb_epoch, n_neu1, n_neu2, dropout):
model = tf.keras.Sequential()
model.add(tf.keras.layers.LSTM(n_neu1, batch_input_shape = (n_batch,X.shape[1],X.shape[2]), return_sequences=True))
model.add(tf.keras.layers.Dropout(dropout))
model.add(tf.keras.layers.LSTM(n_neu2))
model.add(tf.keras.layers.Dropout(dropout))
model.add(tf.keras.layers.Dense(28,activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy','mse'])
for i in range(nb_epoch):
print('epochs : ' + str(i))
model.fit(X,Y, epochs=1, batch_size = n_batch, verbose=1, shuffle=False,callbacks=[custom_hist], validation_data = (Xv,Yv))
model.reset_states()
return model
I tried various neuron number and dropout rate such as
n_batch = 1
nb_epochs = 100
n_neu1 = [128,64,32,16]
n_neu2 = [64,32,16,8]
n_dropout = [0.2,0.4,0.6,0.8]
for dropout in n_dropout:
for i in range(len(n_neu1)):
model = fit_lstm2(x_train,y_train,x_val,y_val,n_batch, nb_epochs,n_neu1[i],n_neu2[i],dropout)
The problem is that the prediction accuracy never goes up more than 10% and over-fitting starts pretty soon after intializing training.
Here are some images of the training histories. (Sorry for the location of the legends)
n_neu1,n_neu2,dropout=(64,32,0.2)
n_neu1,n_neu2,dropout=(32,16,0.2)
n_neu1,n_neu2,dropout=(16,8,0.2)
Honestly, I have no idea why the validation accuracy never goes up and the over-fitting starts so quickly.. Is there better way to use the input data? I mean, should I normalize or standardize the input?
Please help me, any comments and suggestions will be greatly appreciated.

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