def create_model():
units = 512
middle_units = 256
dropout_value = 0.3
activation_function = 'softmax'
loss_function = 'categorical_crossentropy'
optimizer = 'rmsprop'
model = Sequential()
model.add(
LSTM(units,
input_shape=(network_input.shape[1], network_input.shape[2]),
recurrent_dropout=dropout_value,
return_sequences=True))
model.add(
LSTM(
units,
return_sequences=True,
recurrent_dropout=dropout_value,
))
model.add(LSTM(units))
model.add(BatchNormalization())
model.add(Dropout(dropout_value))
model.add(Dense(middle_units))
model.add(Activation('relu'))
model.add(Dropout(dropout_value))
model.add(BatchNormalization())
model.add(Dropout(dropout_value))
model.add(Dense(vocab_size))
model.add(Activation(activation_function))
model.compile(loss=loss_function, optimizer=optimizer)
return model
def create_model_pp():
model = Sequential()
model.add(Flatten(input_shape=(300, 300, 3)))
model.add(Dense(4, activation="softmax"))
model.compile(loss=categorical_crossentropy,
metrics=[categorical_accuracy])
return model
def create_model():
model = Sequential()
model.add(Dense(10, activation="softmax", input_dim=3072))
model.compile(loss=sparse_categorical_crossentropy,
metrics=[sparse_categorical_accuracy])
return model
def create_model():
model = Sequential()
model.add(Dense(128, input_dim=3072)) # 32 * 32 * 3
model.compile(loss=sparse_categorical_crossentropy,
metrics=[sparse_categorical_accuracy])
return model
def free_attn_lstm(dataset_object: LSTM_data):
X_train, X_test, Y_train, Y_test = dataset_object.get_memory()
X_train, X_test = X_train[:, :, :-12], X_test[:, :, :-12]
regressor = Sequential()
# Adding the first LSTM layer and some Dropout regularisation
regressor.add(LSTM(units=NEURONS,
return_sequences=True,
activation=ACTIVATION,
recurrent_activation="sigmoid",
input_shape=(X_train.shape[1], X_train.shape[2]),
bias_regularizer=regularizers.l2(BIAIS_REG),
activity_regularizer=regularizers.l2(L2)
))
regressor.add(Dropout(DROPOUT))
regressor.add(LSTM(units=NEURONS,
activation=ACTIVATION,
recurrent_activation="sigmoid",
return_sequences=True,
bias_regularizer=regularizers.l2(BIAIS_REG),
activity_regularizer=regularizers.l2(L2)
))
regressor.add(Dropout(DROPOUT))
# Adding a second LSTM layer and some Dropout regularisation
regressor.add(LSTM(units=NEURONS,
activation=ACTIVATION,
recurrent_activation="sigmoid",
bias_regularizer=regularizers.l2(BIAIS_REG),
activity_regularizer=regularizers.l2(L2)
))
regressor.add(Dropout(DROPOUT))
# Adding the output layer
regressor.add(Dense(units=1,
activation='relu',
bias_regularizer=regularizers.l2(BIAIS_REG),
activity_regularizer=regularizers.l2(L2)
)
)
optim = Adam()
# Compiling the RNN
regressor.compile(optimizer=optim, loss='mean_squared_error')
# Fitting the RNN to the Training set
history= regressor.fit(X_train,
Y_train,
epochs=EPOCHS,
batch_size=BATCH_SIZE,
validation_data=(X_test, Y_test),
callbacks=[REDUCE_LR, EARLY_STOP]
)
regressor.save("data/weights/free_attn_lstm_no_senti")
plot_train_loss(history)
evaluate(regressor,X_test,Y_test, dataset_object,name="free_attn_lstm", senti="no")
def Train(self, input, target):
X_train, X_test, Y_train, Y_test = train_test_split(input, target, train_size=0.75)
Y_train = np.asarray(Y_train)
Y_test = np.array(Y_test)
X_train = np.reshape(X_train, [-1, X_train[0].shape[0], X_train[0].shape[1]])
X_test = np.reshape(X_test, [-1, X_train[0].shape[0], X_train[0].shape[1]])
model = Sequential()
model.add(Conv1D(16, 3, padding='same', input_shape=input[0].shape))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization())
model.add(GRU(16, return_sequences=True))
# model.add(Activation("sigmoid"))
# model.add(LSTM(lstm_out))
model.add(Flatten())
model.add(Dense(8, activity_regularizer=l2(0.001)))
# model.add(GRU(lstm_out, return_sequences=True))
# model.add(LSTM(lstm_out))
# model.add(Dense(20, activity_regularizer=l2(0.001)))
model.add(Activation("relu"))
model.add(Dense(2))
model.compile(loss=mean_absolute_error, optimizer='nadam',
metrics=[RootMeanSquaredError(), MAE])
print(model.summary())
batch_size = 12
epochs = 100
reduce_lr_acc = ReduceLROnPlateau(monitor='val_loss', factor=0.9, patience=epochs / 10, verbose=1, min_delta=1e-4, mode='max')
model.fit(X_train, Y_train,
epochs=epochs,
batch_size=batch_size, validation_data=(X_test, Y_test), callbacks=[reduce_lr_acc])
model.save("PositionEstimation.h5", overwrite=True)
# acc = model.evaluate(X_test,
# Y_test,
# batch_size=batch_size,
# verbose=0)
predicted = model.predict(X_test, batch_size=batch_size)
# predicted = out.ravel()
res = pd.DataFrame({"predicted_x": predicted[:, 0],
"predicted_y": predicted[:, 1],
"original_x": Y_test[:, 0],
"original_y": Y_test[:, 1]})
res.to_excel("res.xlsx")
def dense_net(dataset_object:LSTM_data):
X_train, X_test, Y_train, Y_test = dataset_object.get_memory()
print(X_test.shape, X_train.shape)
X_train = X_train.reshape(X_train.shape[0],X_train.shape[2])
X_test=X_test.reshape(X_test.shape[0], X_test.shape[2])
X_train, X_test = X_train[:, :-12], X_test[:, :-12]
print(X_test.shape, X_train.shape)
regressor = Sequential()
regressor.add(Dense(units=EPOCHS,
activation='relu',
bias_regularizer=regularizers.l2(BIAIS_REG),
activity_regularizer=regularizers.l2(L2)
))
regressor.add(Dense(units=EPOCHS,
activation='relu',
bias_regularizer=regularizers.l2(BIAIS_REG),
activity_regularizer=regularizers.l2(L2)
))
regressor.add(Dense(units=EPOCHS,
activation='relu',
bias_regularizer=regularizers.l2(BIAIS_REG),
activity_regularizer=regularizers.l2(L2)
))
regressor.add(Dropout(DROPOUT))
regressor.add(Dense(units=1,
activation='relu',
bias_regularizer=regularizers.l2(BIAIS_REG),
activity_regularizer=regularizers.l2(L2)
))
optim = Adam()
# Compiling the RNN
regressor.compile(optimizer=optim, loss='mean_squared_error')
# Fitting the RNN to the Training set
history= regressor.fit(X_train,
Y_train,
epochs=EPOCHS,
batch_size=BATCH_SIZE,
validation_data=(X_test, Y_test),
callbacks=[EARLY_STOP, REDUCE_LR])
regressor.save("data/weights/dense_no_senti")
plot_train_loss(history)
evaluate(regressor, X_test,Y_test, dataset_object,name="dense", senti="yes")
def construct_model(self,
tuned_params: Dict[str, Union[int, float]],
hps: HyperParameters = None) -> Model:
hpf = HyperParameterFactory(self.default_parameters_values,
tuned_params, hps)
max_pool0 = hpf.get_choice(MAXPOOL0_NAME, [1, 2, 4, 8])
max_pool1 = hpf.get_choice(MAXPOOL1_NAME, [1, 2, 4, 8])
max_pool2 = hpf.get_choice(MAXPOOL2_NAME, [1, 2, 4, 8])
filter_0 = hpf.get_choice(FILTER0_NAME, [4, 8, 16, 32])
filter_1 = hpf.get_choice(FILTER1_NAME, [32, 48, 64])
filter_2 = hpf.get_choice(FILTER2_NAME, [64, 96, 128])
dense = hpf.get_int(DENSE_NAME, 32, 128, 8)
lr = hpf.get_choice(LEARNING_RATE_NAME, [1e-2, 1e-3, 1e-4])
model = Sequential([
Input(name='MapView_Input', shape=(43, 39, 7)),
MaxPooling2D(max_pool0, name='MapView_MaxPool_0'),
Conv2D(filter_0,
2,
strides=1,
activation=tf.nn.relu,
name='MapView_Conv2D_1'),
MaxPooling2D(max_pool1, name='MapView_MaxPool_1'),
Conv2D(filter_1,
3,
strides=1,
activation=tf.nn.relu,
name='MapView_Conv2D_2'),
MaxPooling2D(max_pool2, name='MapView_MaxPool_2'),
Conv2D(filter_2,
2,
strides=1,
activation=tf.nn.relu,
name='MapView_Conv2D_3'),
Flatten(name='MapView_Flatten'),
Dropout(0.1, name='MapView_Dropout'),
Dense(dense, activation=tf.nn.relu, name='MapView_Dense'),
Dense(5, activation=tf.nn.softmax, name='MapView_Output'),
])
loss_fn = tf.keras.losses.CategoricalCrossentropy()
opt = tf.keras.optimizers.Adam(learning_rate=lr)
model.compile(optimizer=opt,
loss=loss_fn,
metrics=[tf.keras.metrics.categorical_accuracy])
return model
def get_model(self):
model = Sequential()
model.add(Conv2D(32, kernel_size=(2, 2), activation='relu',
input_shape=(self.feature_dim_1, self.feature_dim_2, self.channel)))
model.add(Conv2D(64, kernel_size=(2, 2), activation='relu'))
model.add(Conv2D(128, kernel_size=(2, 2), activation='relu'))
model.add(MaxPool2D(pool_size=(1, 1)))
model.add(Dropout(0.5))
model.add(Conv2D(128, kernel_size=(2, 2), activation='relu'))
model.add(Conv2D(256, kernel_size=(2, 2), activation='relu'))
model.add(MaxPool2D(pool_size=(1, 1)))
model.add(Dropout(0.5))
model.add(Conv2D(128, kernel_size=(2, 2), activation='relu'))
model.add(Conv2D(256, kernel_size=(4, 4), activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(256, kernel_regularizer=regularizers.l2(0.2), activation='relu'))
model.add(Dense(32, kernel_regularizer=regularizers.l2(0.2), activation='relu'))
model.add(Dense(self.num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='RMSProp', metrics=['accuracy'])
return model
def test_seq_to_seq(self):
#print (self.get_random_states())
train_x = []
# Data size: 10 x (image + 2 actions) x board/action size
train_x = np.random.randint(0, 2, size=(10, 3, 9))
# train_x = [
# [
# [0.1, 1.0],
# [0.1, 1.0],
# [0.1, 1.0],
# [0.1, 1.0],
# [0.1, 1.0],
# ]]
# 1 being the batch size
# 10 being the length
#train_x = np.random.randint(low=0, high=2, size=(1, 10, 9))
train_y = [[0.11, 0.11, 0.11]] * 10
#train_y = [ 0.11 ]
train_y = np.array(train_y)
model = Sequential()
#model.add(layers.Flatten(input_shape=(3, 9))),
#model.add(layers.Embedding(input_shape=(10, 9), ))
model.add(
layers.LSTM(units=100, input_shape=(3, 9), return_sequences=True))
model.add(layers.Dropout(rate=0.25))
model.add(layers.Dense(50, activation='relu'))
model.add(layers.Dense(1, activation=None))
model.compile(optimizer='adam', loss=tf.losses.MSE, metrics=['mae'])
print(model.summary())
model.fit(x=train_x, y=train_y, epochs=100, verbose=0)
loss = model.evaluate(train_x, train_y, verbose=2)
self.assertLess(loss[0], 1e-04)
Lm1 = Sequential()
#q1. try without input?
Lm1.add(Input(shape=(784, )))
Lm1.add(Dense(num_classes, activation='softmax'))
#q2.try SparseCategoricalCrossentropy without one-hot
loss_object = tf.keras.losses.categorical_crossentropy
optimizer = tf.keras.optimizers.SGD(0.01)
#
train_loss = tf.keras.metrics.Mean(name='train_loss')
#try SparseCategoricalAccuracy
train_accuracy = tf.keras.metrics.CategoricalAccuracy(name='train_accuracy')
test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.CategoricalAccuracy(name='test_accuracy')
checkpoint_path = "./checkpoints/"
checkpoint_dir = os.path.dirname(checkpoint_path)
cp_callback = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,
verbose=1,
period=1)
#q3 metrics=xxx without []?
Lm1.compile(optimizer=optimizer, loss=loss_object, metrics=[train_accuracy])
#q4 train_ds?
Lm1.fit(train_ds, epochs=3, callbacks=[cp_callback])
loss, acc = Lm1.evaluate(train_ds)
print("saved model, loss: {:5.2f}, acc: {:5.2f}".format(loss, acc))
class FecModel(Model):
def __init__(self, loader):
self._loader = loader
self._num_train = 28709
self._num_val = 7178
self._batch_size = 64
self._num_epoch = 1
self.create_model()
def create_model(self):
self._model = Sequential()
self._model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(48, 48, 1)))
self._model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
self._model.add(MaxPooling2D(pool_size=(2, 2)))
self._model.add(Dropout(0.25))
self._model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
self._model.add(MaxPooling2D(pool_size=(2, 2)))
self._model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
self._model.add(MaxPooling2D(pool_size=(2, 2)))
self._model.add(Dropout(0.25))
self._model.add(Flatten())
self._model.add(Dense(1024, activation='relu'))
self._model.add(Dropout(0.5))
self._model.add(Dense(7, activation='softmax'))
self._model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.0001, decay=1e-6),
metrics=['accuracy'])
def train_model(self):
print('Load train data...')
train_generator = self._loader.prepare_train_data()
print('Load validation data...')
validation_generator = self._loader.prepare_validation_data()
model_info = self._model.fit_generator(
train_generator,
steps_per_epoch=self._num_train // self._batch_size,
epochs=self._num_epoch,
validation_data=validation_generator,
validation_steps=self._num_val // self._batch_size)
def evaluate_model(self):
print('Load validation data...')
evaluate_generator = self._loader.prepare_validation_data()
model_info = self._model.evaluate(evaluate_generator,
steps=self._num_train // self._batch_size,
batch_size=self._batch_size,
epochs=self._num_epoch)
return model_info
def save_model(self):
print('Save model...')
self._model.save_weights(FecConfig.model_file_name)
def load_model(self):
print('Load model...')
self._model.load_weights(FecConfig.model_file_name)
def make_prediction(self, input_folder):
processed_images = self._loader.prepare_data(input_folder)
print('Predicting data...')
results = []
for processed_image in processed_images:
for face_image in processed_image.face_images:
result = self._model.predict(face_image)
results.append(result)
print('Save results...')
self._loader.save_data(processed_images, results)
class MyAutoEncoder(object):
# archType - 1 => 300|256|300 : archType - 2 => 300|128|300
# archType - 3 => 300|64|300 : archType - 4 => 300|32|300
# archType - 5 => 300|16|300 : archType - 6 => 300|128|64|128|300
# archType - 7 => 300|256|128|128|256|300 : archType - 8 => 300|128|64|32|64|128|300
# archType - 9 => 300||256|128|64|128|256|300 : archType - 10 => 300|128|64|32|16|32|64|128|300
# archType - 11 => 300|256|128|64|32|64|128|256|300 : archType - 12 => 300|256|128|64|32|16|32|64|128|256|300
def __init__(self, logFilePath, inputDim=0, archType=0):
self.logFilePath = logFilePath
if archType == 0: return # We are loading a saved model
# Create auto encoder+decoder
self.autoEncoderModel = Sequential()
self.autoEncoderModel.add(
Dense(inputDim, input_shape=(inputDim, ),
activation='relu')) # Input layer
if archType == 1:
self.autoEncoderModel.add(Dense(256, activation='relu'))
elif archType == 2:
self.autoEncoderModel.add(Dense(128, activation='relu'))
elif archType == 3:
self.autoEncoderModel.add(Dense(64, activation='relu'))
elif archType == 4:
self.autoEncoderModel.add(Dense(32, activation='relu'))
elif archType == 5:
self.autoEncoderModel.add(Dense(16, activation='relu'))
elif archType == 6:
self.autoEncoderModel.add(Dense(128, activation='relu'))
self.autoEncoderModel.add(Dense(64, activation='relu'))
self.autoEncoderModel.add(Dense(128, activation='relu'))
elif archType == 7:
self.autoEncoderModel.add(Dense(256, activation='relu'))
self.autoEncoderModel.add(Dense(128, activation='relu'))
self.autoEncoderModel.add(Dense(256, activation='relu'))
elif archType == 8:
self.autoEncoderModel.add(Dense(128, activation='relu'))
self.autoEncoderModel.add(Dense(64, activation='relu'))
self.autoEncoderModel.add(Dense(32, activation='relu'))
self.autoEncoderModel.add(Dense(64, activation='relu'))
self.autoEncoderModel.add(Dense(128, activation='relu'))
elif archType == 9:
self.autoEncoderModel.add(Dense(256, activation='relu'))
self.autoEncoderModel.add(Dense(128, activation='relu'))
self.autoEncoderModel.add(Dense(64, activation='relu'))
self.autoEncoderModel.add(Dense(128, activation='relu'))
self.autoEncoderModel.add(Dense(256, activation='relu'))
elif archType == 10:
self.autoEncoderModel.add(Dense(128, activation='relu'))
self.autoEncoderModel.add(Dense(64, activation='relu'))
self.autoEncoderModel.add(Dense(32, activation='relu'))
self.autoEncoderModel.add(Dense(16, activation='relu'))
self.autoEncoderModel.add(Dense(32, activation='relu'))
self.autoEncoderModel.add(Dense(64, activation='relu'))
self.autoEncoderModel.add(Dense(128, activation='relu'))
elif archType == 11:
self.autoEncoderModel.add(Dense(256, activation='relu'))
self.autoEncoderModel.add(Dense(128, activation='relu'))
self.autoEncoderModel.add(Dense(64, activation='relu'))
self.autoEncoderModel.add(Dense(32, activation='relu'))
self.autoEncoderModel.add(Dense(64, activation='relu'))
self.autoEncoderModel.add(Dense(128, activation='relu'))
self.autoEncoderModel.add(Dense(256, activation='relu'))
elif archType == 12:
self.autoEncoderModel.add(Dense(256, activation='relu'))
self.autoEncoderModel.add(Dense(128, activation='relu'))
self.autoEncoderModel.add(Dense(64, activation='relu'))
self.autoEncoderModel.add(Dense(32, activation='relu'))
self.autoEncoderModel.add(Dense(16, activation='relu'))
self.autoEncoderModel.add(Dense(32, activation='relu'))
self.autoEncoderModel.add(Dense(64, activation='relu'))
self.autoEncoderModel.add(Dense(128, activation='relu'))
self.autoEncoderModel.add(Dense(256, activation='relu'))
else:
raise ValueError("Incorrect architecture type given.")
self.autoEncoderModel.add(Dense(inputDim,
activation='relu')) # Output layer
self.autoEncoderModel.compile(optimizer='adam', loss=losses.MSE)
self.autoEncoderModel.summary()
# Create encoder
inputSample = Input(shape=(inputDim, ))
inputLayer = self.autoEncoderModel.layers[0]
if 0 < archType < 6:
layerTwo = self.autoEncoderModel.layers[1]
self.encoderModel = Model(inputSample,
layerTwo(inputLayer(inputSample)))
elif archType < 8:
layerTwo = self.autoEncoderModel.layers[1]
layerThree = self.autoEncoderModel.layers[2]
self.encoderModel = Model(
inputSample, layerThree(layerTwo(inputLayer(inputSample))))
elif archType < 10:
layerTwo = self.autoEncoderModel.layers[1]
layerThree = self.autoEncoderModel.layers[2]
layerFour = self.autoEncoderModel.layers[3]
self.encoderModel = Model(
inputSample,
layerFour(layerThree(layerTwo(inputLayer(inputSample)))))
elif archType < 12:
layerTwo = self.autoEncoderModel.layers[1]
layerThree = self.autoEncoderModel.layers[2]
layerFour = self.autoEncoderModel.layers[3]
layerFive = self.autoEncoderModel.layers[4]
self.encoderModel = Model(
inputSample,
layerFive(
layerFour(layerThree(layerTwo(inputLayer(inputSample))))))
elif archType == 12:
layerTwo = self.autoEncoderModel.layers[1]
layerThree = self.autoEncoderModel.layers[2]
layerFour = self.autoEncoderModel.layers[3]
layerFive = self.autoEncoderModel.layers[4]
layerSix = self.autoEncoderModel.layers[5]
self.encoderModel = Model(
inputSample,
layerSix(
layerFive(
layerFour(layerThree(layerTwo(
inputLayer(inputSample)))))))
self.encoderModel.summary()
def train(self, trainX, batchSize, epochs, isDenoising=False):
tic = time.perf_counter()
inputLayer = trainX
if isDenoising: # add some noise to the input layer
inputLayer = trainX + np.random.normal(0, 1, trainX.shape) / 2
self.autoEncoderModel.fit(inputLayer,
trainX,
epochs=epochs,
batch_size=batchSize,
shuffle=True,
validation_split=0.2)
toc = time.perf_counter()
with open(self.logFilePath, "a") as resultsWriter:
resultsWriter.write(
f"AutoEncoder training time: {toc - tic:0.4f} seconds \r")
return toc - tic
def encode(self, dataX, isTrainData):
tic = time.perf_counter()
encodedDataX = self.encoderModel.predict(dataX)
toc = time.perf_counter()
if isTrainData:
with open(self.logFilePath, "a") as resultsWriter:
resultsWriter.write(
f"AutoEncoder training encoding time: {toc - tic:0.4f} seconds \r"
)
else:
with open(self.logFilePath, "a") as resultsWriter:
resultsWriter.write(
f"AutoEncoder testing encoding time: {toc - tic:0.4f} seconds \r\r"
)
return encodedDataX, toc - tic
class CNNClassifier(ClassifierMixin, BaseMultilayerPerceptron):
def __init__(self,
hidden_layer_sizes=(100, ),
activation="relu",
solver='adam',
alpha=0.0001,
batch_size='auto',
learning_rate="constant",
learning_rate_init=0.001,
power_t=0.5,
max_iter=200,
shuffle=True,
random_state=None,
tol=1e-4,
verbose=False,
warm_start=False,
momentum=0.9,
nesterovs_momentum=True,
early_stopping=False,
validation_fraction=0.1,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-8,
n_iter_no_change=10,
max_fun=15000,
conf=None):
super().__init__(hidden_layer_sizes=hidden_layer_sizes,
activation=activation,
solver=solver,
alpha=alpha,
batch_size=batch_size,
learning_rate=learning_rate,
learning_rate_init=learning_rate_init,
power_t=power_t,
max_iter=max_iter,
loss='log_loss',
shuffle=shuffle,
random_state=random_state,
tol=tol,
verbose=verbose,
warm_start=warm_start,
momentum=momentum,
nesterovs_momentum=nesterovs_momentum,
early_stopping=early_stopping,
validation_fraction=validation_fraction,
beta_1=beta_1,
beta_2=beta_2,
epsilon=epsilon,
n_iter_no_change=n_iter_no_change,
max_fun=max_fun)
# Load model
self.conf = conf
self.logger = loggerElk(__name__, True)
# Building the model
self.classifier = Sequential()
# Creating the method for model
# Step 1- Convolution
self.classifier.add(
Convolution2D(128, (5, 5),
input_shape=(self.conf.nn_image_size,
self.conf.nn_image_size, 1),
activation='relu'))
# adding another layer
self.classifier.add(Convolution2D(64, (4, 4), activation='relu'))
# Pooling it
self.classifier.add(MaxPooling2D(pool_size=(2, 2)))
# Adding another layer
self.classifier.add(Convolution2D(32, (3, 3), activation='relu'))
# Pooling
self.classifier.add(MaxPooling2D(pool_size=(2, 2)))
# Adding another layer
self.classifier.add(Convolution2D(32, (3, 3), activation='relu'))
# Pooling
self.classifier.add(MaxPooling2D(pool_size=(2, 2)))
# Step 2- Flattening
self.classifier.add(Flatten())
# Step 3- Full connection
self.classifier.add(Dense(units=128, activation='relu'))
# For the output step
self.classifier.add(
Dense(units=self.conf.nn_class_size, activation='softmax'))
self.classifier.add(Dropout(0.02))
# Add reularizers
# classifier.add(Dense(128,
# input_dim = 128,
# kernel_regularizer = regularizers.l1(0.001),
# activity_regularizer = regularizers.l1(0.001),
# activation = 'relu'))
self.classifier.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
# dropout = classifier.add(Dropout(0.2))
def save_nn(self):
try:
dir_path = os.path.join(self.conf.working_path,
self.conf.nn_model_name)
if os.path.exists(dir_path):
shutil.rmtree(dir_path)
os.makedirs(dir_path)
save_model(self.classifier, filepath=dir_path, overwrite=True)
except Exception as exc:
self.logger.Error(exc)
def load_nn(self):
try:
dir_path = os.path.join(self.conf.working_path,
self.conf.nn_model_name)
self.classifier = load_model(filepath=dir_path)
except Exception as exc:
self.logger.Error(exc)
def fit(self, training_set, validation_set):
"""
Fit the model to data matrix X and target(s) y.
"""
check_pointer = callbacks.ModelCheckpoint(
filepath=self.conf.working_path,
monitor='val_acc',
verbose=1,
save_best_only=True,
save_weights_only=False,
mode='auto',
period=1)
history = self.classifier.fit_generator(
training_set,
steps_per_epoch=(training_set.n / 32),
epochs=self.conf.nn_epochs,
validation_data=validation_set,
validation_steps=(validation_set.n / 32),
callbacks=[check_pointer])
@property
def partial_fit(self):
"""Update the model with a single iteration over the given data.
classes : array, shape (n_classes), default None
Classes across all calls to partial_fit.
Can be obtained via `np.unique(y_all)`, where y_all is the
target vector of the entire dataset.
This argument is required for the first call to partial_fit
and can be omitted in the subsequent calls.
Note that y doesn't need to contain all labels in `classes`.
Returns
-------
self : returns a trained MLP model.
"""
# if self.solver not in _STOCHASTIC_SOLVERS:
# raise AttributeError("partial_fit is only available for stochastic"
# " optimizer. %s is not stochastic"
# % self.solver)
# return self._partial_fit
return
def _partial_fit(self, X, y, classes=None):
# if _check_partial_fit_first_call(self, classes):
# self._label_binarizer = LabelBinarizer()
# if type_of_target(y).startswith('multilabel'):
# self._label_binarizer.fit(y)
# else:
# self._label_binarizer.fit(classes)
#
# super()._partial_fit(X, y)
#
# return self
pass
def _validate_input(self, X, y, incremental):
X, y = check_X_y(X,
y,
accept_sparse=['csr', 'csc', 'coo'],
multi_output=True)
if y.ndim == 2 and y.shape[1] == 1:
y = column_or_1d(y, warn=True)
if not incremental:
self._label_binarizer = LabelBinarizer()
self._label_binarizer.fit(y)
self.classes_ = self._label_binarizer.classes_
elif self.warm_start:
classes = unique_labels(y)
if set(classes) != set(self.classes_):
raise ValueError(
"warm_start can only be used where `y` has "
"the same classes as in the previous "
"call to fit. Previously got %s, `y` has %s" %
(self.classes_, classes))
else:
classes = unique_labels(y)
if len(np.setdiff1d(classes, self.classes_,
assume_unique=True)):
raise ValueError("`y` has classes not in `self.classes_`."
" `self.classes_` has %s. 'y' has %s." %
(self.classes_, classes))
y = self._label_binarizer.transform(y)
return X, y
def predict(self, X):
"""Predict using the multi-layer perceptron classifier
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
The input data.
Returns
-------
y : array-like, shape (n_samples,) or (n_samples, n_classes)
The predicted classes.
"""
# check_is_fitted(self)
# y_pred = self._predict(X)
#
# if self.n_outputs_ == 1:
# y_pred = y_pred.ravel()
#
# return self._label_binarizer.inverse_transform(y_pred)
y_pred = self.classifier.predict(X)
return y_pred
def predict_log_proba(self, X):
"""Return the log of probability estimates.
Parameters
----------
X : array-like, shape (n_samples, n_features)
The input data.
Returns
-------
log_y_prob : array-like, shape (n_samples, n_classes)
The predicted log-probability of the sample for each class
in the model, where classes are ordered as they are in
`self.classes_`. Equivalent to log(predict_proba(X))
"""
# y_prob = self.predict_proba(X)
# return np.log(y_prob, out=y_prob)
pass
def predict_proba(self, X):
"""Probability estimates.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
The input data.
Returns
-------
y_prob : array-like, shape (n_samples, n_classes)
The predicted probability of the sample for each class in the
model, where classes are ordered as they are in `self.classes_`.
"""
check_is_fitted(self)
y_pred = self.classifier.predict_proba(X)
if self.n_outputs_ == 1:
y_pred = y_pred.ravel()
if y_pred.ndim == 1:
return np.vstack([1 - y_pred, y_pred]).T
else:
return y_pred
model.add(Conv2D(32, 3, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, 3, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(5, activation='softmax'))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
print("Training model...")
history = model.fit_generator(
train_data_gen,
steps_per_epoch=int(np.ceil(train_data_gen.n / float(batch_size))),
epochs=EPOCHS,
validation_data=val_data_gen,
validation_steps=int(np.ceil(val_data_gen.n / float(batch_size))))
print("Training complete")
acc = history.history['accuracy'] # tensorflow 1.0 = 'acc', 2.0 = 'accuracy'
val_acc = history.history['val_accuracy']
loss = history.history['loss']