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_simple_mlp():
Asamples = np.random.multivariate_normal([6, 6], [[1, 0], [0, 1]], 200)
Bsamples = np.random.multivariate_normal([1, 1], [[1, 0], [0, 1]], 200)
x_train = np.vstack((Asamples, Bsamples))
y_train = np.vstack((np.array([[0, 1]] * 200), np.array([[1, 0]] * 200)))
print(x_train.shape, y_train.shape)
num_classes = 2
batch_size = 10
epochs = 10
model = Sequential()
model.add(Dense(units=2, activation='relu', input_shape=(2, )))
model.add(Dense(units=num_classes, activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
learning_rate=0.05)
history = model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(None, None))
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)
def test_single_layer():
Asamples = np.random.multivariate_normal([6, 6], [[1, 0], [0, 1]], 200)
Bsamples = np.random.multivariate_normal([1, 1], [[1, 0], [0, 1]], 200)
Csamples = np.random.multivariate_normal([12, 12], [[1, 0], [0, 1]], 200)
# plt.figure()
# plt.plot(Asamples[:, 0], Asamples[:, 1], 'r.')
# plt.plot(Bsamples[:, 0], Bsamples[:, 1], 'b.')
# plt.plot(Csamples[:, 0], Csamples[:, 1], 'g.')
# plt.show()
x_train = np.vstack((Asamples, Bsamples, Csamples))
y_train = np.vstack(
(np.array([[0, 0, 1]] * 200), np.array([[0, 1, 0]] * 200),
np.array([[1, 0, 0]] * 200)))
print(x_train.shape, y_train.shape)
num_classes = 3
batch_size = 20
epochs = 100
model = Sequential()
model.add(Dense(units=num_classes, activation='softmax',
input_shape=(2, )))
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
learning_rate=0.005)
history = model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(None, None))
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='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()