Training script

Author: jaydeepthik

facial_expression.py

@@ -0,0 +1,139 @@
+# -*- coding: utf-8 -*-
+"""
+@authors: jaydeep thik , Vasudev Purandare
+"""
+import tensorflow as tf
+from tensorflow import keras
+#from tensorflow.keras.models import Sequential
+#from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+#---------------------------------------------------------------------------------------------------------------------------------
+def generate_dataset():
+    
+    """generate dataset from csv"""
+    
+    df = pd.read_csv("./fer2013/fer2013.csv")
+    
+    train_samples = df[df['Usage']=="Training"]
+    validation_samples = df[df["Usage"]=="PublicTest"]
+    test_samples = df[df["Usage"]=="PrivateTest"]
+    
+    y_train = train_samples.emotion.astype(np.int32).values
+    y_valid = validation_samples.emotion.astype(np.int32).values
+    y_test = test_samples.emotion.astype(np.int32).values
+     
+    X_train =np.array([ np.fromstring(image, np.uint8, sep=" ").reshape((48,48)) for image in train_samples.pixels])
+    X_valid =np.array([ np.fromstring(image, np.uint8, sep=" ").reshape((48,48)) for image in validation_samples.pixels])
+    X_test =np.array([ np.fromstring(image, np.uint8, sep=" ").reshape((48,48)) for image in test_samples.pixels])
+    
+    
+    return X_train, y_train, X_valid, y_valid, X_test, y_test
+
+#---------------------------------------------------------------------------------------------------------------------------------
+    
+def generate_model(lr=0.001):
+    
+    """training model"""
+    
+    with tf.device('/gpu:0'):  
+        
+        model = keras.models.Sequential()
+        
+        model.add(keras.layers.Conv2D(64,(3,3), input_shape=(48,48, 1), padding="same"))
+        model.add(keras.layers.BatchNormalization())
+        model.add(keras.layers.Activation('relu'))
+        model.add(keras.layers.MaxPooling2D())
+        model.add(keras.layers.Dropout(0.20))
+        
+        model.add(keras.layers.Conv2D(128,(5,5), padding='same'))
+        model.add(keras.layers.BatchNormalization())
+        model.add(keras.layers.Activation('relu'))
+        model.add(keras.layers.MaxPooling2D())
+        model.add(keras.layers.Dropout(0.20))
+
+        model.add(keras.layers.Conv2D(512,(3,3), padding="same"))
+        model.add(keras.layers.BatchNormalization())
+        model.add(keras.layers.Activation('relu'))
+        model.add(keras.layers.MaxPooling2D())
+        model.add(keras.layers.Dropout(0.20))
+        
+        model.add(keras.layers.Conv2D(512,(3,3), padding="same"))
+        model.add(keras.layers.BatchNormalization())
+        model.add(keras.layers.Activation('relu'))
+        model.add(keras.layers.MaxPooling2D())
+        model.add(keras.layers.Dropout(0.25))
+        
+        model.add(keras.layers.Conv2D(256,(3,3), activation='relu', padding='same'))
+        model.add(keras.layers.Conv2D(128,(3,3), padding='same', activation='relu'))
+        model.add(keras.layers.MaxPooling2D())
+        model.add(keras.layers.Dropout(0.25))
+        
+        #model.add(keras.layers.GlobalAveragePooling2D())
+        model.add(keras.layers.Flatten())
+        model.add(keras.layers.Dense(256))
+        model.add(keras.layers.BatchNormalization())
+        model.add(keras.layers.Activation('relu'))
+        model.add(keras.layers.Dropout(0.5))
+        
+        model.add(keras.layers.Dense(512, activation='relu'))
+        model.add(keras.layers.BatchNormalization())
+        model.add(keras.layers.Activation('relu'))
+        model.add(keras.layers.Dropout(0.5))
+        
+        model.add(keras.layers.Dense(4,activation='softmax'))
+        
+        model.compile(loss="categorical_crossentropy", optimizer=keras.optimizers.Adam(lr=lr) , metrics=['accuracy'])
+        return model
+    
+#---------------------------------------------------------------------------------------------------------------------------------
+        
+if __name__=="__main__":
+    
+    #df = pd.read_csv("./fer2013/fer2013.csv")
+   # X_train, y_train, X_valid, y_valid, X_test, y_test =  generate_dataset()
+    
+   # X_train = X_train.reshape((-1,48,48,1)).astype(np.float32)
+   # X_valid = X_valid.reshape((-1,48,48,1)).astype(np.float32)
+    #X_test = X_test.reshape((-1,48,48,1)).astype(np.float32)
+    
+   # X_train_std = X_train/255.
+    
+   #X_valid_std = X_valid/255.
+    #X_test_std = X_test/255.
+    
+    
+    train_datagen = keras.preprocessing.image.ImageDataGenerator(
+    rescale=1./255,
+    shear_range=0.2,
+    zoom_range=0.2,
+    horizontal_flip=True)
+
+    test_datagen = keras.preprocessing.image.ImageDataGenerator(rescale=1./255)
+
+    train_generator = train_datagen.flow_from_directory(
+    'hackdataset/train',
+    target_size=(48, 48),
+    color_mode='grayscale',
+    batch_size=128,
+    class_mode='categorical')
+
+    test_generator = test_datagen.flow_from_directory(
+    'hackdataset/test',
+    target_size=(48, 48),
+    color_mode='grayscale',
+    batch_size=128,
+    class_mode='categorical')
+    
+    
+    model = generate_model(0.01)
+    with tf.device("/gpu:0"):
+        #history = model.fit(X_train_std, y_train,batch_size=128,epochs=35, validation_data=(X_valid_std, y_valid), shuffle=True)
+        history = model.fit_generator(train_generator, steps_per_epoch=200,epochs=35, validation_data=test_generator, validation_steps=80)
+    
+    
+    
+    
+