def test_mlp_mnist():
train_set, test_set = mnist(one_hot=True)
x_train, y_train = train_set[0], train_set[1]
x_test, y_test = test_set[0], test_set[1]
x_train = x_train.reshape(x_train.shape[0],
x_train.shape[1] * x_train.shape[2])
x_test = x_test.reshape(x_test.shape[0], x_test.shape[1] * x_test.shape[2])
num_classes = 10
batch_size = 32
epochs = 1
model = Sequential()
model.add(Dense(units=256, activation='relu', input_shape=(784, )))
model.add(Dense(units=128, activation='relu'))
model.add(Dense(units=64, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer='momentum',
learning_rate=0.05)
history = model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
def test_mlp():
model = Sequential()
model.add(Dense(units=512, activation='relu', input_shape=(784, )))
model.add(Dense(units=512, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy', optimizer='RMSprop')
history = model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(None, None))
score = model.evaluate(x_test, y_test, verbose=0)
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer='RMSprop',
learning_rate=0.05,
metrics=['train_loss', 'val_loss'])
history = model.fit(
x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_val, y_val),
# validation_split=0.2,
)
print(history.history)
try:
import matplotlib.pyplot as plt
plt.plot(history.history['train_loss'])
plt.plot(history.history['val_loss'])
plt.title('Loss over epochs')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train loss', 'Validation loss'], loc='best')
plt.show()
except Exception as e:
print(e)
score = model.evaluate(x_test, y_test, verbose=0)