model.add(Dropout(0.25))
model.add(BatchNormalization())
model.add(Dense(10, activation='relu')) # 10 digits classes
model.compile(loss='cce', optimizer=opt)
model.summary()
model_epochs = 5
fit_stats = model.fit(train_data,
one_hot(train_label),
batch_size=128,
epochs=model_epochs,
validation_data=(test_data, one_hot(test_label)),
shuffle_data=True)
eval_stats = model.evaluate(test_data, one_hot(test_label))
predictions = unhot(model.predict(test_data, True))
print_results(predictions, test_label)
plot_img_results(test_data[:40], test_label[:40], predictions,
dataset='mnist') # truncate to 40 samples
model_name = model.model_name
plot_metric('loss',
model_epochs,
fit_stats['train_loss'],
fit_stats['valid_loss'],
model_name=model_name)
plot_metric('accuracy',
model_epochs,
fit_stats['train_acc'],
model.add(Dropout(0.2))
model.add(BatchNormalization())
model.add(Dense(100))
model.add(Activation('softmax'))
model.compile(loss = 'cce', optimizer = opt)
model.summary(model_name = 'cifar-100 mlp')
model_epochs = 12 # change to 200 epochs
fit_stats = model.fit(reshaped_train_data,
one_hot(train_label),
batch_size = 128,
epochs = model_epochs,
validation_data = (reshaped_test_data, one_hot(test_label)),
shuffle_data = True)
eval_stats = model.evaluate(reshaped_test_data, one_hot(test_label))
predictions = unhot(model.predict(reshaped_test_data, True))
print_results(predictions, test_label)
plot_img_results(test_data, test_label, predictions, dataset = 'cifar', channels = 3)
plot_metric('loss', model_epochs, fit_stats['train_loss'], fit_stats['valid_loss'], model_name = model.model_name)
plot_metric('accuracy', model_epochs, fit_stats['train_acc'], fit_stats['valid_acc'], model_name = model.model_name)
plot_metric('evaluation',
eval_stats['valid_batches'],
eval_stats['valid_loss'],
eval_stats['valid_acc'],
model_name = model_name,
legend = ['loss', 'acc'])
model.add(Dense(10))
model.add(Activation('softmax'))
model.compile(loss='cce', optimizer=opt)
model.summary(model_name='cifar-10 mlp')
model_epochs = 200 # change to 200 epochs
fit_stats = model.fit(transformed_train_data,
one_hot(train_label),
batch_size=128,
epochs=model_epochs,
validation_data=(transformed_test_data,
one_hot(test_label)),
shuffle_data=True)
eval_stats = model.evaluate(transformed_test_data, one_hot(test_label))
predictions = unhot(model.predict(transformed_test_data, True))
print_results(predictions, test_label)
plot_img_results(test_data,
test_label,
predictions,
dataset='cifar',
channels=3)
model_name = model.model_name
plot_metric('loss',
model_epochs,
fit_stats['train_loss'],
fit_stats['valid_loss'],
model_name=model_name)
