File size: 4,059 Bytes
f5586d9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 |
"""
This function manages the general RNN architecture
"""
import os
import random
import numpy as np
from keras import backend as K
from keras.callbacks import EarlyStopping, TensorBoard
# Environment settings
IS_COLAB = (os.name == 'posix')
LOAD_DATA = not (os.name == 'posix')
if IS_COLAB:
from datetime import datetime
from packaging import version
# Define the Keras TensorBoard callback.
logdir = "logs/scalars/" + datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = TensorBoard(log_dir=logdir)
class RecurrentNeuralNetwork(object):
def __init__(self, seed):
"""
Seed - The seed used to initialize the weights
width, height, cells - used for defining the tensors used for the input images
loss, metrics, optimizer, dropout_rate - settings used for compiling the siamese model (e.g., 'Accuracy' and 'ADAM)
"""
K.clear_session()
self.seed = seed
self.initialize_seed()
self.model = None
def initialize_seed(self):
"""
Initialize seed all for environment
"""
os.environ['PYTHONHASHSEED'] = str(self.seed)
random.seed(self.seed)
np.random.seed(self.seed)
def _load_weights(self, weights_file):
"""
A function that attempts to load pre-existing weight files for the siamese model. If it succeeds then returns
True and updates the weights, otherwise False.
:return True if the file is already exists
"""
self.model.summary()
self.load_file = weights_file
if os.path.exists(weights_file): # if the file is already exists, load and return true
print('Loading pre-existed weights file')
self.model.load_weights(weights_file)
return True
return False
def fit(self, weights_file, batch_size, epochs, patience, min_delta, x_train, y_train, x_val, y_val):
"""
Function for fitting the model. If the weights already exist, just return the summary of the model. Otherwise,
perform a whole train/validation/test split and train the model with the given parameters.
"""
# Create callbacks
if not self._load_weights(weights_file=weights_file):
print('No such pre-existed weights file')
print('Beginning to fit the model')
if IS_COLAB:
callbacks = [
tensorboard_callback,
EarlyStopping(monitor='val_loss', patience=patience, min_delta=min_delta)
]
else:
callbacks = [
EarlyStopping(monitor='val_loss', patience=patience, min_delta=min_delta)
]
self.model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
callbacks=callbacks,
validation_data=(x_val, y_val))
self.model.save_weights(self.load_file)
# evaluate on the validation set
loss, accuracy = self.model.evaluate(x_val, y_val, batch_size=batch_size)
print(f'Loss on Validation set: {loss}')
print(f'Accuracy on Validation set: {accuracy}')
def evaluate(self, x_test, y_test, batch_size):
"""
Function for evaluating the final model after training.
test_file - file path to the test file.
batch_size - the batch size used in training.
Returns the loss and accuracy results.
"""
print(f'Available Metrics: {self.model.metrics_names}')
y_test = np.array(y_test, dtype='float64')
x_test[0] = np.array(x_test[0], dtype='float64')
x_test[1] = np.array(x_test[1], dtype='float64')
# evaluate on the test set
loss, accuracy = self.model.evaluate(x_test, y_test, batch_size=batch_size)
return loss, accuracy
def predict(self, data):
return self.model.predict(data)
print('Loaded Successfully')
|