Digit Recognizer (using ANN)
Steps to be followed:
1. Gather the data (MINST dataset)
2. Normalize the dataset
3. Neural network architecture
4. Compile the model
5. Fit the model (Train the model)
6. Evaluate the model
7. Save the model ( Architecture, model weights)
8. Use the model in streamlit canvas.
Code to create a model:
import numpy as np
from tensorflow import keras
from keras import layers
import matplotlib.pyplot as plt
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 0s 0us/step
print(x_train.shape)
print(x_test.shape)
(60000, 28, 28)
(10000, 28, 28)
print(y_train)
print(y_test)
[5 0 4 ... 5 6 8]
[7 2 1 ... 4 5 6]
# lets see some random images and its labels
import random
import matplotlib.pyplot as plt
i = random.randint(0,60000)
plt.imshow(x_train[i],cmap='gray') # Color map
plt.title([y_train[i]])
plt.show()
# How many images are there in every digit?
import numpy as np
np.unique(y_train,return_counts=True)
Out[ ]:
(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8),
array([5923, 6742, 5958, 6131, 5842, 5421, 5918, 6265, 5851, 5949]))
np.unique(y_test,return_counts=True)
Out[ ]:
(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8),
array([ 980, 1135, 1032, 1010, 982, 892, 958, 1028, 974, 1009]))
x_train[0]
Out[ ]:
array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3,
18, 18, 18, 126, 136, 175, 26, 166, 255, 247, 127, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 30, 36, 94, 154, 170,
253, 253, 253, 253, 253, 225, 172, 253, 242, 195, 64, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 49, 238, 253, 253, 253, 253,
253, 253, 253, 253, 251, 93, 82, 82, 56, 39, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 18, 219, 253, 253, 253, 253,
253, 198, 182, 247, 241, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 80, 156, 107, 253, 253,
205, 11, 0, 43, 154, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 1, 154, 253,
90, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 139, 253,
190, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 190,
253, 70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 35,
241, 225, 160, 108, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
81, 240, 253, 253, 119, 25, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 45, 186, 253, 253, 150, 27, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 16, 93, 252, 253, 187, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 249, 253, 249, 64, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 46, 130, 183, 253, 253, 207, 2, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 39,
148, 229, 253, 253, 253, 250, 182, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 114, 221,
253, 253, 253, 253, 201, 78, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 23, 66, 213, 253, 253,
253, 253, 198, 81, 2, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 18, 171, 219, 253, 253, 253, 253,
195, 80, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 55, 172, 226, 253, 253, 253, 253, 244, 133,
11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 136, 253, 253, 253, 212, 135, 132, 16, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0]], dtype=uint8)
# Normalization : Scaling down the value to a specific range(0-1)
x_train=x_train/255
x_test = x_test/255
# AFter Normalization
print(x_train.max())
print(x_train.min())
1.0
0.0
from keras.layers import Dense
from keras.layers import Flatten
model = keras.Sequential()
model.add(Flatten(input_shape=(28,28)))
model.add(Dense(392,activation='relu'))
model.add(Dense(10,activation='softmax'))
model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])
history = model.fit(x_train,y_train,epochs=10,validation_split=0.2)
Epoch 1/10
1500/1500 [==============================] - 7s 4ms/step - loss: 0.3801 - accuracy: 0.8910 - val_loss: 0.1204 - val_accuracy: 0.9633
Epoch 2/10
1500/1500 [==============================] - 6s 4ms/step - loss: 0.0983 - accuracy: 0.9702 - val_loss: 0.0867 - val_accuracy: 0.9736
Epoch 3/10
1500/1500 [==============================] - 6s 4ms/step - loss: 0.0563 - accuracy: 0.9836 - val_loss: 0.0848 - val_accuracy: 0.9741
Epoch 4/10
1500/1500 [==============================] - 6s 4ms/step - loss: 0.0385 - accuracy: 0.9882 - val_loss: 0.0798 - val_accuracy: 0.9773
Epoch 5/10
1500/1500 [==============================] - 6s 4ms/step - loss: 0.0283 - accuracy: 0.9913 - val_loss: 0.0794 - val_accuracy: 0.9772
Epoch 6/10
1500/1500 [==============================] - 6s 4ms/step - loss: 0.0220 - accuracy: 0.9930 - val_loss: 0.0882 - val_accuracy: 0.9758
Epoch 7/10
1500/1500 [==============================] - 6s 4ms/step - loss: 0.0164 - accuracy: 0.9949 - val_loss: 0.0793 - val_accuracy: 0.9781
Epoch 8/10
1500/1500 [==============================] - 6s 4ms/step - loss: 0.0123 - accuracy: 0.9962 - val_loss: 0.0939 - val_accuracy: 0.9770
Epoch 9/10
1500/1500 [==============================] - 6s 4ms/step - loss: 0.0100 - accuracy: 0.9969 - val_loss: 0.1072 - val_accuracy: 0.9739
Epoch 10/10
1500/1500 [==============================] - 6s 4ms/step - loss: 0.0095 - accuracy: 0.9972 - val_loss: 0.1056 - val_accuracy: 0.9755
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
Out[ ]:
[<matplotlib.lines.Line2D at 0x7f3539b32850>]
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
Out[ ]:
[<matplotlib.lines.Line2D at 0x7f3538b14fd0>]
# Evaluate on test data
y_pred = model.predict(x_test)
y_pred = np.argmax(y_pred,axis=1)
y_pred
Out[ ]:
array([7, 2, 1, ..., 4, 5, 6])
from sklearn.metrics import accuracy_score,confusion_matrix,classification_report
accuracy_score(y_pred,y_test)
Out[ ]:
0.9764
confusion_matrix(y_pred,y_test)
Out[ ]:
array([[ 973, 0, 3, 2, 2, 2, 5, 1, 4, 2],
[ 1, 1126, 0, 0, 0, 0, 2, 4, 1, 2],
[ 0, 5, 1017, 10, 2, 0, 1, 9, 11, 1],
[ 1, 0, 1, 982, 1, 9, 1, 1, 10, 0],
[ 0, 0, 1, 0, 952, 0, 2, 1, 5, 4],
[ 0, 0, 0, 3, 0, 866, 3, 0, 2, 1],
[ 2, 2, 2, 0, 4, 5, 944, 0, 6, 0],
[ 0, 0, 7, 3, 2, 2, 0, 1000, 4, 2],
[ 2, 2, 0, 0, 1, 2, 0, 1, 907, 0],
[ 1, 0, 1, 10, 18, 6, 0, 11, 24, 997]])
print(classification_report(y_pred,y_test))
precision recall f1-score support
0 0.99 0.98 0.99 994
1 0.99 0.99 0.99 1136
2 0.99 0.96 0.97 1056
3 0.97 0.98 0.97 1006
4 0.97 0.99 0.98 965
5 0.97 0.99 0.98 875
6 0.99 0.98 0.98 965
7 0.97 0.98 0.98 1020
8 0.93 0.99 0.96 915
9 0.99 0.93 0.96 1068
accuracy 0.98 10000
macro avg 0.98 0.98 0.98 10000
weighted avg 0.98 0.98 0.98 10000
In [ ]:
# save the model
model.save('mnist.hdf5')
Use this model in the canvas:
Output:
Example code(github): Click here
NN)
