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84 KiB
84 KiB
Книга для обучения GCAEC классификатора с использованием Tensorflow из статьи https://gitlab.com/protsenkovi/efd_nn/
In [1]:
import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' import random import tensorflow as tf # tf.autograph.set_verbosity(1) # tf.get_logger().setLevel('INFO') import tensorflow_addons as tfa from IPython.display import display, clear_output import seaborn as sns import pandas as pd import numpy as np import warnings from tensorflow.keras import regularizers from tensorflow.keras.callbacks import LearningRateScheduler # os.environ["CUDA_VISIBLE_DEVICES"]="-1" from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Dropout, Reshape, SimpleRNN, GRU, LSTM, PReLU, MaxPooling1D, Flatten, AveragePooling1D, \ GaussianNoise from tensorflow.keras.layers import Conv1D, BatchNormalization from tensorflow.keras.optimizers import Adam, Adamax, SGD from tensorflow.keras import losses from tensorflow.keras import metrics as kmetrics from tensorflow.keras.saving import load_model from tensorflow.keras.utils import plot_model from sklearn.neural_network import MLPClassifier warnings.filterwarnings('ignore') from sklearn.metrics import confusion_matrix import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split, StratifiedKFold from time import time np.random.seed(42) random.seed(42) clear_output() tf.keras.utils.set_random_seed(42) tf.config.experimental.enable_op_determinism() print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU'))) gpus = tf.config.list_physical_devices('GPU') if gpus: try: # Currently, memory growth needs to be the same across GPUs for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True) logical_gpus = tf.config.list_logical_devices('GPU') print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs") except RuntimeError as e: # Memory growth must be set before GPUs have been initialized print(e) print(tf.__version__)
Num GPUs Available: 1 1 Physical GPUs, 1 Logical GPUs 2.12.0
In [2]:
window = 24 X_train = np.load(f"../assets/X_train_{window}.npy") y_train = np.load(f"../assets/y_train_{window}.npy") X_val = np.load(f"../assets/X_val_{window}.npy") y_val = np.load(f"../assets/y_val_{window}.npy") X_test = np.load(f"../assets/X_test_{window}.npy") y_test = np.load(f"../assets/y_test_{window}.npy") shuffled_index = np.arange(X_train.shape[0]) np.random.shuffle(shuffled_index) X_train = X_train[shuffled_index] y_train = y_train[shuffled_index] shuffled_index = np.arange(X_val.shape[0]) np.random.shuffle(shuffled_index) X_val = X_val[shuffled_index] y_val = y_val[shuffled_index] shuffled_index = np.arange(X_test.shape[0]) np.random.shuffle(shuffled_index) X_test = X_test[shuffled_index] y_test = y_test[shuffled_index] X_train.shape, X_val.shape, X_test.shape
Out[2]:
((116600, 24, 6), (29150, 24, 6), (11054, 24, 6))
In [3]:
import tensorflow.keras.layers as kl import tensorflow.keras as k def new_pred(predict): pred = [] for i in predict: if i < 0.5: pred.append(0) else: pred.append(1) return pred conv_num = -1 def conv_block_name(): global conv_num conv_num = conv_num + 1 return 'cb_{}'.format(conv_num) def conv_block(output_timesteps, output_channels, kernel_size, name, activity_regu=None): def f(preceding_layer): input_timesteps, input_channels = preceding_layer.get_shape().as_list()[1:] inputs = k.Input(shape=(input_timesteps, input_channels)) act = kl.Conv1D(output_channels, kernel_size=kernel_size, activation='linear', padding='same', name=name+'_conv_features1', activity_regularizer=activity_regu)(inputs) gate = kl.Conv1D(output_channels, kernel_size=kernel_size, activation='sigmoid', padding='same', name=name+'_conv_memory')(inputs) gated_act = kl.Multiply()([tfa.layers.InstanceNormalization()(kl.PReLU()(act)), gate]) a = kl.Permute((2,1))(gated_act) b = kl.Dense(output_timesteps, use_bias=False)(a) c = kl.Permute((2,1))(b) m = k.Model(inputs=inputs, outputs=c, name=name) # m.summary() return m(preceding_layer) return f def model_1D(input_shape): inputs = k.Input(shape=input_shape) e = conv_block(output_timesteps=window//2, output_channels=128, kernel_size=3, name=conv_block_name())(inputs) e = conv_block(output_timesteps=2, output_channels=128, kernel_size=3, name=conv_block_name(), activity_regu=regularizers.l1(1e-3))(e) c = kl.Flatten()(e) c = Dense(1, activation='sigmoid', name='sigmoid_layer')(c) d = conv_block(output_timesteps=window//2, output_channels=128, kernel_size=3, name=conv_block_name())(e) d = conv_block(output_timesteps=input_shape[0], output_channels=input_shape[1], kernel_size=3, name=conv_block_name())(d) decoder_output = kl.GaussianNoise(1e-2, name='decoder_output')(d) classifier_model = k.Model(inputs=inputs, outputs=c, name='classifier') model = k.Model(inputs=inputs, outputs=[decoder_output, c], name='autoencoder') return model, classifier_model class ProgressCallback(k.callbacks.Callback): def on_epoch_end(self, epoch, logs={}): results = pd.DataFrame(data=np.array([v for k,v in logs.items()]).reshape(2,4), columns=['loss', 'ae_loss', 'classifier_loss', 'accuracy'], index=['train','val']) results = results.style.set_caption(f"{epoch}") clear_output(wait=True) display(results) def train(X_train, y_train, X_val, y_val, model, epochs=50, lr=1e-2): model.compile(loss={ 'decoder_output':losses.MSE, 'sigmoid_layer':losses.BinaryCrossentropy() }, optimizer=Adam(learning_rate=lr), metrics={ 'sigmoid_layer': kmetrics.BinaryAccuracy(), }) try: history = model.fit(X_train, [X_train, y_train], epochs=epochs, batch_size=4096, verbose=0, validation_data=(X_val, (X_val, y_val)), callbacks=[ProgressCallback()]) except KeyboardInterrupt as e: history = [] return history
In [4]:
gcaec, classifier = model_1D(input_shape=X_train.shape[1:]) gcaec.summary()
Model: "autoencoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 24, 6)] 0 []
cb_0 (Functional) (None, 12, 128) 8480 ['input_1[0][0]']
cb_1 (Functional) (None, 2, 128) 100376 ['cb_0[0][0]']
cb_2 (Functional) (None, 12, 128) 99096 ['cb_1[0][0]']
cb_3 (Functional) (None, 24, 6) 4992 ['cb_2[0][0]']
flatten (Flatten) (None, 256) 0 ['cb_1[0][0]']
decoder_output (GaussianNoise) (None, 24, 6) 0 ['cb_3[0][0]']
sigmoid_layer (Dense) (None, 1) 257 ['flatten[0][0]']
==================================================================================================
Total params: 213,201
Trainable params: 213,201
Non-trainable params: 0
__________________________________________________________________________________________________
In [5]:
start = time() history = train(X_train=X_train, y_train=y_train, X_val=X_test, y_val=y_test, model=gcaec, lr=1e-4, epochs=1000) print("\nTraining time = ", (time() - start) / 60)
| loss | ae_loss | classifier_loss | accuracy | |
|---|---|---|---|---|
| train | 0.003677 | 0.002197 | 0.000171 | 1.000000 |
| val | 4.735565 | 0.002891 | 4.731774 | 0.619776 |
Training time = 11.216969986756643
In [6]:
f, [a1,a2] = plt.subplots(2,1) a1.plot(history.history['loss'], label='loss') a1.plot(history.history['val_loss'], label='val_loss') a1.set_yscale('log') a1.legend() a2.plot(history.history['sigmoid_layer_binary_accuracy'], label='accuracy') a2.plot(history.history['val_sigmoid_layer_binary_accuracy'], label='val_accuracy') a2.set_yscale('log') a2.legend()
Out[6]:
<matplotlib.legend.Legend at 0x7f71403e7fa0>
In [7]:
threshold = 0.5
In [8]:
pred_train = classifier.predict(X_train) > threshold conf_train = confusion_matrix(y_train, pred_train) acc_train = (conf_train[0][0] + conf_train[1][1]) / (conf_train[0][0] + conf_train[1][1] + conf_train[0][1] + conf_train[1][0]) f1_train = (2 * conf_train[1][1]) / (2 * conf_train[1][1] + conf_train[0][1] + conf_train[1][0]) print('Train Accuracy: ', acc_train) print('F1: ', f1_train)
3644/3644 [==============================] - 8s 2ms/step Train Accuracy: 1.0 F1: 1.0
In [9]:
pred_val = classifier.predict(X_val) > threshold conf_val = confusion_matrix(y_val, pred_val) acc_val = (conf_val[0][0] + conf_val[1][1]) / (conf_val[0][0] + conf_val[1][1] + conf_val[0][1] + conf_val[1][0]) f1_val = (2 * conf_val[1][1]) / (2 * conf_val[1][1] + conf_val[0][1] + conf_val[1][0]) print('Val Accuracy: ', acc_val) print('F1: ', f1_val)
911/911 [==============================] - 2s 2ms/step Val Accuracy: 0.9884734133790738 F1: 0.9885464957731115
In [10]:
predict_cnn = classifier.predict(X_test) > threshold conf_test = confusion_matrix(y_test, predict_cnn) acc_test = (conf_test[0][0] + conf_test[1][1]) / ( conf_test[0][0] + conf_test[1][1] + conf_test[0][1] + conf_test[1][0]) f1_test = (2 * conf_test[1][1]) / (2 * conf_test[1][1] + conf_test[0][1] + conf_test[1][0]) print('Test Accuracy: ', acc_test) print('F1: ', f1_test)
346/346 [==============================] - 1s 2ms/step Test Accuracy: 0.6197756468246789 F1: 0.45969919012726573
In [11]:
aa = pd.DataFrame(data=conf_test, columns=['Norm', 'Anom'], index=['Norm', 'Anom']) sns.heatmap(aa, annot=True, cmap=sns.blend_palette(['#f5f0f0','#e8a7a8'], as_cmap=True));
In [12]:
model_file_path = f"../results/gcaec_{window}.h5" classifier.save(model_file_path, save_format="h5") pd.DataFrame( data=[[model_file_path, acc_train, f1_train, acc_val, f1_val, acc_test, f1_test]], columns=['model', 'acc_train', 'f1_train', 'acc_val', 'f1_val', 'acc_test', 'f1_test'] ).to_csv(f"{model_file_path}_stats.csv", index=False)
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
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