I try to train a CNN model that classifies the handwritten digit using Keras, but I am getting low accuracy in the training (lower than 10%) and a big error. I tried a simple neural network without concolutions and it didn't work as well.
This is my code.
import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
#Explore data
print(y_train[12])
print(np.shape(x_train))
print(np.shape(x_test))
#we have 60000 imae for the training and 10000 for testing
# Scaling data
x_train = x_train/255
y_train = y_train/255
#reshape the data
x_train = x_train.reshape(60000,28,28,1)
x_test = x_test.reshape(10000,28,28,1)
y_train = y_train.reshape(60000,1)
y_test = y_test.reshape(10000,1)
#Create a model
model = keras.Sequential([
keras.layers.Conv2D(64,(3,3),(1,1),padding = "same",input_shape=(28,28,1)),
keras.layers.MaxPooling2D(pool_size = (2,2),padding = "valid"),
keras.layers.Conv2D(32,(3,3),(1,1),padding = "same"),
keras.layers.MaxPooling2D(pool_size = (2,2),padding = "valid"),
keras.layers.Flatten(),
keras.layers.Dense(128,activation = "relu"),
keras.layers.Dense(10,activation = "softmax")])
model.compile(optimizer = "adam",
loss = "sparse_categorical_crossentropy",
metrics = ['accuracy'])
model.fit(x_train,y_train,epochs=10)
test_loss,test_acc = model.evaluate(x_test,y_test)
print("\ntest accuracy:",test_acc)
Could anyone advice me on how to improve my model?
Your problem is here:
x_train = x_train/255
y_train = y_train/255 # makes no sense
You should have rescaled x_test, not y_train.
x_train = x_train/255
x_test = x_test/255
That was probably just a typo from your part. Change these lines and you'll have 95%+ accuracy.