customize_cnn.py

from keras.models import Sequential
from keras.layers import Conv2D, MaxPool2D, Dropout, Flatten, Dense
from keras.utils import to_categorical
import matplotlib.pyplot as plt
import numpy as np
import pydot_ng as pydot
import streamlit as st
from keras.datasets import fashion_mnist
st.title('Customizable Convolutional Neural Network')
st.header('Dataset: Fashion MNIST')
st.text("This page allows you to build and train a CNN for the fashion MNIST dataset via an interactive visual interface")

# train test split
(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
# reshape dataset to have a single channel
x_train = x_train.reshape((x_train.shape[0], 28, 28, 1))
x_test = x_test.reshape((x_test.shape[0], 28, 28, 1))
# boolean on whether to show images
if st.checkbox('Show images sizes'):
    st.write(f'X Train Shape: {x_train.shape}')
    st.write(f'X Test Shape: {x_test.shape}')
    st.write(f'Y Train Shape: {y_train.shape}')
    st.write(f'Y Test Shape: {y_test.shape}')

# display some random images
classes = ['T-shirt/top', 'Trouser/pants', 'Pullover shirt', 'Dress','Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
st.subheader('Inspect dataset')
if st.checkbox('Display random image from the train set'):
    num = np.random.randint(0, x_train.shape[0])
    img = x_train[num]
    st.image(img, caption=classes[y_train[num]], use_column_width=True)

# interactive hyperparameter settings
st.subheader('Set some hyperparameters for model tuning')
batch_size = st.selectbox('Select batch size', [32, 64, 128, 256])
epochs=st.selectbox('Select number of epochs', [10, 25, 50])
loss_function = st.selectbox('Loss function', ['mean_squared_error', 'mean_absolute_error', 'categorical_crossentropy'])
optimizer = st.selectbox('Optimizer', ['SGD', 'RMSprop', 'Adam'])

# generating CNN
st.subheader('Building your Custom CNN')
st.text('Default Kernel size is the suggested 3X3')
model = Sequential()
activation_1 = st.selectbox('Activation function for first layer: ', ['relu', 'tanh', 'softmax'])

model.add(Conv2D(32,kernel_size=(3,3),activation=activation_1, padding='same', input_shape=(28,28,1)))
model.add(MaxPool2D(pool_size=(2,2)))

if st.checkbox('Add hidden Conv2D layer?'):
    activation_h = st.selectbox('Activation function for hidden layer: ', ['relu', 'tanh', 'softmax'])
    model.add(Conv2D(64, kernel_size=(3, 3), activation=activation_h, padding='same', input_shape=(28,28,1)))
    model.add(MaxPool2D(pool_size=(2, 2)))

if st.checkbox('Add a drop layer?'):
    drop1=st.selectbox('Select dropout Rate', [0.1, 0.25, 0.5])
    model.add(Dropout(drop1))
model.add(Flatten())
activation_2 = st.selectbox('Activation function for Dense layer: ', ['relu', 'tanh', 'softmax'])
model.add(Dense(512,activation=activation_2))
activation_3 = st.selectbox('Activation function for Output layer: ', ['relu', 'tanh', 'softmax'])
model.add(Dense(10,activation=activation_3))

# model compiling
model.compile(loss=loss_function, optimizer=optimizer, metrics=['accuracy'])
if st.checkbox('Fit model'):
    history = model.fit(x_train[0:1000]/255.0, to_categorical(y_train[0:1000]),
         batch_size=batch_size,
         shuffle=True,
         epochs=epochs,
         validation_data=(x_test[0:1000]/255.0, to_categorical(y_test[0:1000]))
         )

    # Plot training & validation accuracy values
    plt.plot(history.history['acc'])
    plt.plot(history.history['val_acc'])
    plt.title('Model accuracy')
    plt.ylabel('Accuracy')
    plt.xlabel('Epoch')
    plt.legend(['Train', 'Test'], loc='upper left')
    st.pyplot()
predictions = model.predict(x_test / 255.0)
scores = model.evaluate(x_test / 255.0, to_categorical(y_test))

st.write(f'loss: {round(scores[0], 2)}')
st.write(f'accuracy: {round(100 * scores[1], 2)}%')

# visualize predictions
st.subheader('Visualize Predictions')

def plot_pred(i, predictions_array, true_label, img):
    predictions_array, true_label, img = predictions_array[i], true_label[i:i + 1], img[i]
    plt.grid(False)
    plt.title(classes[true_label[0]])
    plt.xticks([])
    plt.yticks([])

    plt.imshow(img)

# plot the 10 classes and their probabilities
def plot_bar(i, predictions_array, true_label):
    predictions_array, true_label = predictions_array[i], true_label[i]
    plt.grid(False)
    plt.yticks([])
    plt.xticks(np.arange(10), classes, rotation=40)

    plot = plt.bar(range(10), predictions_array, color='grey')
    plt.ylim([0, 1])
    predicted_label = np.argmax(predictions_array)

    if predicted_label == true_label:
        color = 'green'
    else:
        color = 'red'

    plot[predicted_label].set_color(color)


if st.checkbox('Show random prediction results'):
    num2 = np.random.randint(0, len(y_test))
    plt.figure(figsize=(15, 6))
    plt.subplot(1, 2, 1)
    plot_pred(num2, predictions, y_test, x_test)
    plt.subplot(1, 2, 2)
    plot_bar(num2, predictions, y_test)
    st.pyplot()