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number_of_tracks_classification.py
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number_of_tracks_classification.py
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import datasets.number_of_tracks as task
from pp_cm import pp_matrix
from sklearn.metrics import confusion_matrix
import numpy as np
import tensorflow as tf
from tensorflow import keras
from keras import layers # Dense, Droupout, Softmax, BatchNormalization, Conv2D
from keras.callbacks import EarlyStopping
import seaborn as sn
import matplotlib.pyplot as plt
def get_dataset_partitions(ds, ds_size, train_split=0.8, val_split=0.1):
print(ds_size)
train_size = int(train_split * ds_size)
val_size = int(val_split * ds_size)
train_ds = ds.take(train_size)
val_ds = ds.skip(train_size).take(val_size)
test_ds = ds.skip(train_size).skip(val_size)
return train_ds, val_ds, test_ds
def architecture():
'''
'''
i = keras.Input(shape=(9,113))
x = layers.Reshape((9,113,1))(i)
x = layers.Conv2D(4,3,activation = 'relu',padding="same")(x)
x = layers.MaxPooling2D(pool_size = (2,2))(x)
x = layers.Conv2D(16,3,activation = 'relu',padding="same")(x)
x = layers.MaxPooling2D(pool_size=(2,2))(x)
x = layers.Conv2D(64,3,activation = 'relu',padding="same")(x)
x = layers.MaxPooling2D(pool_size = (2,2))(x)
x = layers.Dropout(0.2)(x)
x = layers.Flatten()(x)
x = layers.Dense(64,activation='relu',use_bias=True)(x)
x = layers.Dense(4)(x)
x = layers.Softmax()(x)
model = keras.Model(inputs = i, outputs = x)
return model
def architecture1():
'''
'''
i = keras.Input(shape=(9,113))
x = layers.Reshape((9,113,1))(i)
x = layers.Conv2D(4,(3,10),activation = 'relu',padding="same")(x) # 4
x = layers.MaxPooling2D(pool_size = (1,2),strides=(1,2))(x)
x = layers.Conv2D(16,(3,10),activation = 'relu',padding="same")(x) # 16
x = layers.MaxPooling2D(pool_size = (1,2),strides=(1,2))(x)
x = layers.Conv2D(64,(3,3),activation = 'relu',padding="same")(x) # 64
x = layers.MaxPooling2D(pool_size = (2,2))(x)
x = layers.Conv2D(128,(3,3),activation = 'relu',padding="same")(x) # 128
x = layers.MaxPooling2D(pool_size = (2,2))(x)
x = layers.Dropout(0.2)(x)
x = layers.Flatten()(x)
x = layers.Dense(128,activation='relu',use_bias=True)(x)
x = layers.Dense(4)(x)
x = layers.Softmax()(x)
model = keras.Model(inputs = i, outputs = x)
return model
def architecture2():
'''
'''
i = keras.Input(shape=(9,113))
img = layers.Reshape((9,113,1))(i)
x = layers.Conv2D(16,(3,15),activation = 'relu',padding="same")(img)
x = layers.MaxPooling2D(pool_size = (1,2),strides=(1,2))(x)
x = layers.Conv2D(128,(3,7),activation = 'relu',padding="same")(x)
x = layers.MaxPooling2D(pool_size = (1,2),strides=(1,2))(x)
x = layers.Conv2D(256,(3,3),activation = 'relu',padding="same")(x)
x = layers.MaxPooling2D(pool_size = (2,2))(x)
x = layers.Conv2D(256,(3,3),activation = 'relu',padding="same")(x)
x = layers.MaxPooling2D(pool_size = (2,2))(x)
x = layers.Conv2D(256,(3,3),activation = 'relu',padding="same")(x)
x = layers.MaxPooling2D(pool_size = (2,2))(x)
x = layers.Dropout(0.2)(x)
x = layers.Flatten()(x)
x = layers.Dense(128,activation='relu',use_bias=True)(x)
x = layers.Dense(64,activation='sigmoid',use_bias=True)(x)
x = layers.Dense(4)(x)
x = layers.Softmax()(x)
model = keras.Model(inputs = i, outputs = x)
return model
def architectureHeight():
'''
'''
i = keras.Input(shape=(30,113))
img = layers.Reshape((30,113,1))(i)
x = layers.Conv2D(16,(3,7),activation = 'relu',padding="same")(img) # 16
x = layers.MaxPooling2D(pool_size = (1,2),strides=(1,2))(x)
x = layers.Conv2D(64,(3,3),activation = 'relu',padding="same")(x) # 64
x = layers.MaxPooling2D(pool_size = (2,2))(x)
x = layers.Conv2D(256,(3,3),activation = 'relu',padding="same")(x) # 128
x = layers.MaxPooling2D(pool_size = (2,2))(x)
x = layers.Conv2D(256,(3,3),activation = 'relu',padding="same")(x) # delete
x = layers.MaxPooling2D(pool_size = (2,2))(x) # delete
x = layers.Dropout(0.2)(x)
x = layers.Flatten()(x)
x = layers.Dense(256,activation='relu',use_bias=True)(x)
x = layers.Dense(64,activation='sigmoid',use_bias=True)(x)
x = layers.Dense(4)(x)
x = layers.Softmax()(x)
model = keras.Model(inputs = i, outputs = x)
return model
def architectureFront():
'''
'''
i = keras.Input(shape=(30,9))
img = layers.Reshape((30,9,1))(i)
x = layers.Conv2D(16,(6,2),activation = 'relu',padding="same")(img)
x = layers.Conv2D(64,(6,2),activation = 'relu',padding="same")(x)
x = layers.MaxPooling2D(pool_size = (3,1),strides=(6,2))(x)
x = layers.Conv2D(128,(2,2),activation = 'relu',padding="same")(x) # 64
x = layers.Dropout(0.2)(x)
x = layers.Flatten()(x)
x = layers.Dense(256,activation='relu',use_bias=True)(x)
x = layers.Dense(64,activation='sigmoid',use_bias=True)(x)
x = layers.Dense(4)(x)
x = layers.Softmax()(x)
model = keras.Model(inputs = i, outputs = x)
return model
def plot_train_val_accuracy(history):
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='upper left')
plt.savefig(
fname = 'training_accuracy.pdf',
format = 'pdf'
)
def confusion(model, test_dataset):
y_true = []
for _,label in test_dataset:
y_true.append(tf.argmax(label))
print("Creating confusion matrix")
prediction=model.predict(test_dataset.batch(256))
prediction = np.argmax(prediction, axis=1)
cm = confusion_matrix(prediction, y_true)
tf.print(cm,summarize=-1)
# sn.heatmap(cm, annot=True, annot_kws={"size": 16})
# plt.savefig("confusion_matrix.pdf")
if __name__=="__main__":
print(tf.config.list_physical_devices())
'''
'''
tracks = 4
files=10
events = 5000
model = architectureHeight()
keras.utils.plot_model(model = model,to_file = "model.png",show_shapes= True,show_dtype = True)
# visualizer(
# model = model,
# filename="graph.pdf",
# format='pdf',
# view = False
# )
dataset_size = tracks*files*events
dataset = tf.data.Dataset.from_generator(
generator = lambda: task.generator(tracks,[0,1,2,3,4,5,6,7],events),
output_signature=(tf.TensorSpec(shape=(3),dtype=tf.int64))
)
dataset = dataset.shuffle(dataset_size,reshuffle_each_iteration = True)
dataset = dataset.map(task.load_event)
print(dataset)
# for i in dataset:
# tf.print(i,summarize=-1)
val_dataset = tf.data.Dataset.from_generator(
generator = lambda: task.generator(tracks,[8],events),
output_signature=(tf.TensorSpec(shape=(3),dtype=tf.int64))
)
val_dataset = val_dataset.map(task.load_event)
model.compile(
optimizer = 'adam',
loss = tf.keras.losses.CategoricalCrossentropy(),
metrics = ['accuracy']
)
history = model.fit(
x = dataset.batch(1024),
epochs = 15,
validation_data = val_dataset.batch(1024),
callbacks=[EarlyStopping(monitor='val_accuracy',mode='max',baseline=0.9,start_from_epoch=6,min_delta=0.01)]
)
plot_train_val_accuracy(history)
test_dataset = tf.data.Dataset.from_generator(
generator = lambda: task.generator(tracks,[9],events),
output_signature=(tf.TensorSpec(shape=(3),dtype=tf.int64))
)
test_dataset = test_dataset.map(task.load_event)
confusion(model,test_dataset)
model.save("model")