si_lab07

from keras.layers import Conv2D, Flatten, Dense
from keras.models import Sequential
from keras.optimizers import Adam
from keras.datasets import mnist
import numpy as np
train, test = mnist.load_data()
X_train, y_train = train[0], train[1]
X_test, y_test = test[0], test[1]
X_train = np.expand_dims(X_train, axis=-1)
X_test = np.expand_dims(X_test, axis=-1)
class_cnt = np.unique(y_train).shape[0]
filter_cnt = 16
neuron_cnt = 16
learning_rate = 0.001
act_func = 'relu'
kernel_size = (3,3)
model = Sequential()
conv_rule = 'same'
model.add(Conv2D(input_shape = X_train.shape[1:], filters=filter_cnt, kernel_size = kernel_size, padding = conv_rule, activation = act_func))
model.add(Flatten())
model.add(Dense(class_cnt, activation='softmax'))
model.compile(optimizer=Adam(learning_rate),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])


from keras.layers import Conv2D, Flatten, Dense, AveragePooling2D, MaxPooling2D
filter_cnt = 32
neuron_cnt = 32
learning_rate = 0.0001
act_func = 'relu'
kernel_size = (3,3)
pooling_size = (2,2)
model = Sequential()
conv_rule = 'same'
model.add(Conv2D(input_shape = X_train.shape[1:], filters=filter_cnt, kernel_size = kernel_size, padding = conv_rule, activation = act_func))
model.add(MaxPooling2D(pooling_size))
model.add(Flatten())
model.add(Dense(class_cnt, activation='softmax'))
model.compile(optimizer=Adam(learning_rate),loss='SparseCategoricalCrossentropy',metrics='accuracy')
model.fit(x = X_train, y = y_train, epochs =     class_cnt, validation_data=(X_test, y_test))