Data Fitting Using Tensorflow and Keras

import sys, os  
import pandas as pd  
import numpy as np  
from google.colab import files
uploaded = files.upload() 

from keras.models import Sequential  
from keras.layers import Dense, Dropout, Activation, Flatten  
from keras.layers import Conv2D, MaxPooling2D, BatchNormalization,AveragePooling2D  
from keras.losses import categorical_crossentropy  
from keras.optimizers import Adam  
from keras.regularizers import l2  
from keras.utils import np_utils  
# pd.set_option('display.max_rows', 500)  
# pd.set_option('display.max_columns', 500)  
# pd.set_option('display.width', 1000)  
  
df=pd.read_csv('icml_face_data.csv')  
  
# print(df.info())  
# print(df["Usage"].value_counts())  
  
# print(df.head())  
X_train,train_y,X_test,test_y=[],[],[],[]  
  
for index, row in df.iterrows():  
    val=row['pixels'].split(" ")  
    try:  
        if 'Training' in row['Usage']:  
           X_train.append(np.array(val,'float32'))  
           train_y.append(row['emotion'])  
        elif 'PublicTest' in row['Usage']:  
           X_test.append(np.array(val,'float32'))  
           test_y.append(row['emotion'])  
    except:  
        print(f"error occured at index :{index} and row:{row}")  
  
  
num_features = 64  
num_labels = 7  
batch_size = 64  
epochs = 30  
width, height = 48, 48  
  
  
X_train = np.array(X_train,'float32')  
train_y = np.array(train_y,'float32')  
X_test = np.array(X_test,'float32')  
test_y = np.array(test_y,'float32')  
  
train_y=np_utils.to_categorical(train_y, num_classes=num_labels)  
test_y=np_utils.to_categorical(test_y, num_classes=num_labels)  
  
#cannot produce  
#normalizing data between oand 1  
X_train -= np.mean(X_train, axis=0)  
X_train /= np.std(X_train, axis=0)  
  
X_test -= np.mean(X_test, axis=0)  
X_test /= np.std(X_test, axis=0)  
  
X_train = X_train.reshape(X_train.shape[0], 48, 48, 1)  
  
X_test = X_test.reshape(X_test.shape[0], 48, 48, 1)  
  
# print(f"shape:{X_train.shape}")  
##designing the cnn  
#1st convolution layer  
model = Sequential()  
  
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', input_shape=(X_train.shape[1:])))  
model.add(Conv2D(64,kernel_size= (3, 3), activation='relu'))  
# model.add(BatchNormalization())  
model.add(MaxPooling2D(pool_size=(2,2), strides=(2, 2)))  
model.add(Dropout(0.5))  
  
#2nd convolution layer  
model.add(Conv2D(64, (3, 3), activation='relu'))  
model.add(Conv2D(64, (3, 3), activation='relu'))  
# model.add(BatchNormalization())  
model.add(MaxPooling2D(pool_size=(2,2), strides=(2, 2)))  
model.add(Dropout(0.5))  
  
#3rd convolution layer  
model.add(Conv2D(128, (3, 3), activation='relu'))  
model.add(Conv2D(128, (3, 3), activation='relu'))  
# model.add(BatchNormalization())  
model.add(MaxPooling2D(pool_size=(2,2), strides=(2, 2)))  
  
model.add(Flatten())  
  
#fully connected neural networks  
model.add(Dense(1024, activation='relu'))  
model.add(Dropout(0.2))  
model.add(Dense(1024, activation='relu'))  
model.add(Dropout(0.2))  
  
model.add(Dense(num_labels, activation='softmax'))  
  
# model.summary()  
  
#Compliling the model  
model.compile(loss=categorical_crossentropy,  
              optimizer=Adam(),  
              metrics=['accuracy'])  
  
#Training the model  
model.fit(X_train, train_y,  
          batch_size=batch_size,  
          epochs=epochs,  
          verbose=1,  
          validation_data=(X_test, test_y),  
          shuffle=True)  
model_json = model.to_json()
with open("model.json", "w") as json_file:
  json_file.write(model_json)
  model.save_weights("model.h5")
  print("Saved model to disk")