def build_discriminator(self, optimizer, loss_function):
model = NeuralNetwork(optimizer=optimizer, loss=loss_function)
model.add(
Conv2D(32,
filter_shape=(3, 3),
stride=2,
input_shape=self.img_shape,
padding='same'))
model.add(Activation('leaky_relu'))
model.add(Dropout(0.25))
model.add(Conv2D(64, filter_shape=(3, 3), stride=2, padding='same'))
model.add(ZeroPadding2D(padding=((0, 1), (0, 1))))
model.add(Activation('leaky_relu'))
model.add(Dropout(0.25))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(128, filter_shape=(3, 3), stride=2, padding='same'))
model.add(Activation('leaky_relu'))
model.add(Dropout(0.25))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(256, filter_shape=(3, 3), stride=1, padding='same'))
model.add(Activation('leaky_relu'))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(2))
model.add(Activation('softmax'))
return model
def build_encoder(self, optimizer, loss_function):
encoder = NeuralNetwork(optimizer=optimizer, loss=loss_function)
encoder.add(Dense(512, input_shape=(self.img_dim, )))
encoder.add(Activation('leaky_relu'))
encoder.add(BatchNormalization(momentum=0.8))
encoder.add(Dense(256))
encoder.add(Activation('leaky_relu'))
encoder.add(BatchNormalization(momentum=0.8))
encoder.add(Dense(self.latent_dim))
return encoder
def build_generator(self, optimizer, loss_function):
model = NeuralNetwork(optimizer=optimizer, loss=loss_function)
model.add(Dense(256, input_shape=(self.latent_dim,)))
model.add(Activation('leaky_relu'))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(512))
model.add(Activation('leaky_relu'))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(1024))
model.add(Activation('leaky_relu'))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(self.img_dim))
model.add(Activation('tanh'))
return model
def build_generator(self, optimizer, loss_function):
model = NeuralNetwork(optimizer=optimizer, loss=loss_function)
model.add(Dense(128 * 7 * 7, input_shape=(100, )))
model.add(Activation('leaky_relu'))
model.add(Reshape((128, 7, 7)))
model.add(BatchNormalization(momentum=0.8))
model.add(UpSampling2D())
model.add(Conv2D(128, filter_shape=(3, 3), padding='same'))
model.add(Activation("leaky_relu"))
model.add(BatchNormalization(momentum=0.8))
model.add(UpSampling2D())
model.add(Conv2D(64, filter_shape=(3, 3), padding='same'))
model.add(Activation("leaky_relu"))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(1, filter_shape=(3, 3), padding='same'))
model.add(Activation("tanh"))
return model
def main():
#----------
# Conv Net
#----------
optimizer = Adam()
data = datasets.load_digits()
X = data.data
y = data.target
# Convert to one-hot encoding
y = to_categorical(y.astype("int"))
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.4,
seed=1)
# Reshape X to (n_samples, channels, height, width)
X_train = X_train.reshape((-1, 1, 8, 8))
X_test = X_test.reshape((-1, 1, 8, 8))
clf = NeuralNetwork(optimizer=optimizer,
loss=CrossEntropy,
validation_data=(X_test, y_test))
clf.add(
Conv2D(n_filters=16,
filter_shape=(3, 3),
stride=1,
input_shape=(1, 8, 8),
padding='same'))
clf.add(Activation('relu'))
clf.add(Dropout(0.25))
clf.add(BatchNormalization())
clf.add(Conv2D(n_filters=32, filter_shape=(3, 3), stride=1,
padding='same'))
clf.add(Activation('relu'))
clf.add(Dropout(0.25))
clf.add(BatchNormalization())
clf.add(Flatten())
clf.add(Dense(256))
clf.add(Activation('relu'))
clf.add(Dropout(0.4))
clf.add(BatchNormalization())
clf.add(Dense(10))
clf.add(Activation('softmax'))
print()
clf.summary(name="ConvNet")
train_err, val_err = clf.fit(X_train, y_train, n_epochs=50, batch_size=256)
# Training and validation error plot
n = len(train_err)
training, = plt.plot(range(n), train_err, label="Training Error")
validation, = plt.plot(range(n), val_err, label="Validation Error")
plt.legend(handles=[training, validation])
plt.title("Error Plot")
plt.ylabel('Error')
plt.xlabel('Iterations')
plt.show()
_, accuracy = clf.test_on_batch(X_test, y_test)
print("Accuracy:", accuracy)
y_pred = np.argmax(clf.predict(X_test), axis=1)
X_test = X_test.reshape(-1, 8 * 8)
# Reduce dimension to 2D using PCA and plot the results
Plot().plot_in_2d(X_test,
y_pred,
title="Convolutional Neural Network",
accuracy=accuracy,
legend_labels=range(10))
