Binary Classifier on Particle Track Data

In [1]:

    

%matplotlib inline


    

In [2]:

    

import pandas as pd
import matplotlib.pyplot as plt
import os
import sys
import numpy as np
import math


    

In [3]:

    

track_params = pd.read_csv('../TRAIN/track_parms.csv')


    

In [4]:

    

track_params.tail()


    

    
Out[4]:







  

    

      

      
filename
      
phi
      
z
      
phi_calc
      
phi_regression
      
sigma_regression
    
  
  

    

      
499995
      
img499995.png
      
-4.509653
      
0.968584
      
-4.494071
      
-4.494040
      
0.014105
    
    

      
499996
      
img499996.png
      
-1.595661
      
-7.397094
      
-1.645501
      
-1.645483
      
0.014181
    
    

      
499997
      
img499997.png
      
7.695264
      
-2.984060
      
7.799254
      
7.799186
      
0.013931
    
    

      
499998
      
img499998.png
      
-1.898667
      
5.082713
      
-1.880849
      
-1.880834
      
0.014177
    
    

      
499999
      
img499999.png
      
4.275843
      
-2.266920
      
4.324348
      
4.324317
      
0.014112
    
  

In [5]:

    

# Create binary labels
track_params['phi_bool'] = track_params.phi.apply(lambda x: "+" if x > 0 else "-")


    

In [6]:

    

track_params[['phi', 'z']].hist()


    

    
Out[6]:





array([[<matplotlib.axes._subplots.AxesSubplot object at 0x104512128>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x118d9e588>]],
      dtype=object)






    

In [7]:

    

track_params.plot(x='z', y='phi', kind='hexbin', sharex=False, cmap="Blues")


    

    
Out[7]:





<matplotlib.axes._subplots.AxesSubplot at 0x116734e10>






    

In [8]:

    

track_params.head()


    

    
Out[8]:







  

    

      

      
filename
      
phi
      
z
      
phi_calc
      
phi_regression
      
sigma_regression
      
phi_bool
    
  
  

    

      
0
      
img000000.png
      
-0.195900
      
-5.164839
      
-0.206930
      
-0.206928
      
0.014192
      
-
    
    

      
1
      
img000001.png
      
-1.473349
      
5.784543
      
-1.409622
      
-1.409614
      
0.014184
      
-
    
    

      
2
      
img000002.png
      
9.206585
      
-2.295192
      
9.296442
      
9.296330
      
0.016293
      
+
    
    

      
3
      
img000003.png
      
5.378890
      
4.685070
      
5.281532
      
5.281474
      
0.014072
      
+
    
    

      
4
      
img000004.png
      
-6.700401
      
-0.851756
      
-6.739551
      
-6.739504
      
0.013997
      
-
    
  

In [9]:

    

from tensorflow.keras.preprocessing.image import ImageDataGenerator


    

In [11]:

    

DATAGEN = ImageDataGenerator(rescale=1./255.,
                             validation_split=0.25)


    

In [12]:

    

height = 100
width = 36

def create_generator(target, subset, class_mode,
                     idg=DATAGEN, df=track_params, N=1000):
    
    return idg.flow_from_dataframe(
        dataframe=track_params.head(N),
        directory="../TRAIN",
        x_col="filename",
        y_col=target,
        subset=subset,
        target_size=(height, width),
        batch_size=32,
        seed=314,
        shuffle=True,
        class_mode=class_mode,
    )


    

In [13]:

    

binary_train_generator = create_generator(
    target="phi_bool",
    subset="training",
    class_mode="binary"
)
binary_val_generator = create_generator(
    target="phi_bool",
    subset="validation",
    class_mode="binary"
)


    

    




Found 750 validated image filenames belonging to 2 classes.
Found 250 validated image filenames belonging to 2 classes.

In [14]:

    

from tensorflow.keras import Sequential, Model
from tensorflow.keras.layers import (
    Conv2D, Activation, MaxPooling2D,
    Flatten, Dense, Dropout, Input
)


    

In [15]:

    

width  = 36
height = 100
channels = 3

def binary_classifier():
    model = Sequential()

    # Convolutional Block Layer
    model.add(Conv2D(32, (3, 3), input_shape=(height, width, channels)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    # Dense, Classification Layer
    model.add(Flatten())
    model.add(Dense(32))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(1))
    model.add(Activation('sigmoid'))

    model.compile(loss='binary_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy'])
    
    return model


    

In [16]:

    

STEP_SIZE_TRAIN = binary_train_generator.n//binary_train_generator.batch_size
STEP_SIZE_VAL = binary_val_generator.n//binary_val_generator.batch_size


    

In [17]:

    

binary_model = binary_classifier()
binary_history = binary_model.fit_generator(
    generator=binary_train_generator,
    steps_per_epoch=STEP_SIZE_TRAIN,
    validation_data=binary_val_generator,
    validation_steps=STEP_SIZE_VAL,
    epochs=5
)


    

    




WARNING: Logging before flag parsing goes to stderr.
W0815 06:45:40.222872 4567270848 deprecation.py:323] From /Users/dannowitz/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where






    




Epoch 1/5
23/23 [==============================] - 2s 77ms/step - loss: 0.1190 - accuracy: 0.9777 - val_loss: 9.2283e-05 - val_accuracy: 1.0000
Epoch 2/5
23/23 [==============================] - 1s 30ms/step - loss: 6.3248e-04 - accuracy: 1.0000 - val_loss: 2.6192e-05 - val_accuracy: 1.0000
Epoch 3/5
23/23 [==============================] - 1s 31ms/step - loss: 8.2586e-04 - accuracy: 1.0000 - val_loss: 1.3146e-05 - val_accuracy: 1.0000
Epoch 4/5
23/23 [==============================] - 1s 31ms/step - loss: 8.7263e-04 - accuracy: 1.0000 - val_loss: 3.5262e-06 - val_accuracy: 1.0000
Epoch 5/5
23/23 [==============================] - 1s 30ms/step - loss: 2.1041e-04 - accuracy: 1.0000 - val_loss: 2.1267e-06 - val_accuracy: 1.0000

In [19]:

    

plt.plot(binary_history.history['accuracy'], label="Train Accuracy")
plt.plot(binary_history.history['val_accuracy'], label="Validation Accuracy")
plt.legend()
plt.show()