def build_pilotnet_model(self):
def resize(images):
import tensorflow as tf
return tf.image.resize_area(images, size=(66, 200))
def preprocessing(images):
import tensorflow as tf
standardization = lambda x: tf.image.per_image_standardization(x)
normalized_rgb = tf.math.divide(images, 255.0)
augmented_img_01 = tf.image.random_brightness(normalized_rgb,
max_delta=0.4)
augmented_img_02 = tf.image.random_contrast(augmented_img_01,
lower=0.5,
upper=1.5)
std_img = tf.map_fn(standardization, augmented_img_02)
return std_img
cropping_top, cropping_down = self.cropping_top, self.cropping_down
# Build PilotNet model
inputs = keras.Input(shape=(160, 320, 3), name='inputs')
cropping = keras.layers.Cropping2D(cropping=((cropping_top,
cropping_down), (0, 0)),
name='cropping')(inputs)
resize = keras.layers.Lambda(resize, name='resize')(cropping)
norm = keras.layers.Lambda(preprocessing, name='normalization')(resize)
# input = (66, 200, 3), output = (31, 98, 24)
conv_1 = keras.layers.Conv2D(filters=24,
kernel_size=5,
strides=2,
padding='valid',
name='conv_1')(norm)
bn_1 = keras.layers.BatchNormalization(name='bn_1')(conv_1)
relu_1 = keras.layers.ReLU(name='relu_1')(bn_1)
# input = (31, 98, 24), output = (14, 47, 36)
conv_2 = keras.layers.Conv2D(filters=36,
kernel_size=5,
strides=2,
padding='valid',
name='conv_2')(relu_1)
bn_2 = keras.layers.BatchNormalization(name='bn_2')(conv_2)
relu_2 = keras.layers.ReLU(name='relu_2')(bn_2)
# input = (14, 47, 36), output = (5, 22, 48)
conv_3 = keras.layers.Conv2D(filters=48,
kernel_size=5,
strides=2,
padding='valid',
name='conv_3')(relu_2)
bn_3 = keras.layers.BatchNormalization(name='bn_3')(conv_3)
relu_3 = keras.layers.ReLU(name='relu_3')(bn_3)
# input = (5, 22, 48), output = (3, 20, 64)
conv_4 = keras.layers.Conv2D(filters=64,
kernel_size=3,
strides=1,
padding='valid',
name='conv_4')(relu_3)
bn_4 = keras.layers.BatchNormalization(name='bn_4')(conv_4)
relu_4 = keras.layers.ReLU(name='relu_4')(bn_4)
# input = (3, 20, 64), output = (1, 18, 64)
conv_5 = keras.layers.Conv2D(filters=64,
kernel_size=3,
strides=1,
padding='valid',
name='conv_5')(relu_4)
bn_5 = keras.layers.BatchNormalization(name='bn_5')(conv_5)
relu_5 = keras.layers.ReLU(name='relu_5')(bn_5)
# input = (1, 18, 64), output = (1152,)
flatten = keras.layers.Flatten(name='flatten')(relu_5)
dropout_5 = keras.layers.Dropout(rate=0.5, name='dropout_5')(flatten)
# input = (1152,), output = (100,)
dense_6 = keras.layers.Dense(units=100, name='dense_6')(dropout_5)
bn_6 = keras.layers.BatchNormalization(name='bn_6')(dense_6)
relu_6 = keras.layers.ReLU(name='relu_6')(bn_6)
dropout_6 = keras.layers.Dropout(rate=0.5, name='dropout_6')(relu_6)
# input = (100,), output = (50,)
dense_7 = keras.layers.Dense(units=50, name='dense_7')(dropout_6)
bn_7 = keras.layers.BatchNormalization(name='bn_7')(dense_7)
relu_7 = keras.layers.ReLU(name='relu_7')(bn_7)
dropout_7 = keras.layers.Dropout(rate=0.5, name='dropout_7')(relu_7)
# input = (50,), output = (10,)
dense_8 = keras.layers.Dense(units=10, name='dense_8')(dropout_7)
bn_8 = keras.layers.BatchNormalization(name='bn_8')(dense_8)
relu_8 = keras.layers.ReLU(name='relu_8')(bn_8)
dropout_8 = keras.layers.Dropout(rate=0.5, name='dropout_8')(relu_8)
# input = (10,), output = (1,)
outputs = keras.layers.Dense(units=1, name='outputs')(dropout_8)
# set up cropping2D layer
self.model = keras.Model(inputs=inputs, outputs=outputs)
self.model.compile(
optimizer=keras.optimizers.Adam(0.01),
loss=keras.losses.logcosh,
# loss='mse',
metrics=['mse', 'mae'])
def iter_conv(data_count, enc_dim, x_train, y_train, train_deltas, epochs=1):
#resets backend variables
K.clear_session()
################### define model #####################
input_dim = (1, 25, 25)
input_frame = keras.Input(shape=input_dim)
#x = keras.layers.Dense(enc_dim)(input_frame)
x = layers.Conv2D(filters=32, kernel_size=(3, 3),
padding='same')(input_frame) #(x)
#x = keras.layers.BatchNormalization()(x)
x = layers.Conv2D(filters=128,
kernel_size=(3, 3),
activation='relu',
padding='same')(x)
x = layers.BatchNormalization()(x)
x = layers.Conv2D(filters=32,
kernel_size=(3, 3),
activation='relu',
padding='same')(x)
x = layers.BatchNormalization()(x)
x = layers.Conv2D(filters=64,
kernel_size=(3, 3),
activation='relu',
padding='same')(x)
out_layer = layers.Dense(25, activation='relu')(x)
model = keras.models.Model(input_frame, out_layer)
model.summary()
##################### begin training #####################
# good resource for creating a custom training routine (what this is based on)
# https://gist.github.com/JVGD/2add7789ab83588a397bbae6ff614dbf
optimizer = keras.optimizers.Adam(lr=0.001)
#loss = custom_loss(5)#5 assumes a constant delta of 5 across all data
loss = keras.losses.categorical_crossentropy(
keras.Input(shape=input_dim), model(keras.Input(shape=input_dim)))
update_op = optimizer.get_updates(params=model.trainable_weights,
loss=loss)
'''loss(keras.Input(shape=input_dim),
model(keras.Input(shape=input_dim))
))#converts input from numpy to tensor'''
train = K.function(
inputs=[x_train, y_train],
outputs=loss, #outputs=[loss,model.layer[-1].output],
updates=update_op)
test = K.function(inputs=[x_train, y_train], outputs=[loss])
for epoch in range(epochs):
training_losses = []
for cur_sample in range(data_count):
#TODO apply train_deltas to loop rather than constant 5
sample_delta = 5 #train_deltas[cur_sample]
#loop to feedback output for delta time steps of prediction
sample = x_train[cur_sample]
target = y_train[cur_sample]
#add batch size as dimension
sample = np.expand_dims(sample, axis=0)
target = np.expand_dims(target, axis=0)
#convert to tensors
sample = K.constant(sample)
target = K.constant(target)
cur_input = cur_sample
#target = tf.convert_to_tensor(target)
for i in range(sample_delta):
#cur_input = tf.convert_to_tensor(cur_input)
#calculate loss, running a training iteration
loss_train = train([
tf.convert_to_tensor(cur_input),
tf.convert_to_tensor(target)
])
training_losses.append(loss_train[0])
#set next input to current output (out_layer.output)
cur_input = model.predict(cur_input)
train_loss_mean = np.mean(training_losses)
print("Epoch ", epoch, "; Current mean training loss: ",
train_loss_mean)
'''
#Now compute test values (no training)
losses_test = []
for cur_sample in range(data_count)'''
'''
model = keras.Model(inputs=input_frame,outputs=out_layer)
model.compile(loss=Loss,
optimizer='adam',
metrics=['accuracy'])
'''
return model
import tensorflow as tf
print(tf.__version__)
tf.test.gpu_device_name()
#if you see something like '/device:GPU:0' then the gpu accelerated learning will be enabled. if not, cpu will work, just slower.
"""##Our Neural Network
Here's what our network will look like. Pretty straight foward, 4 layers of convolutions. 64 filters per layer. A 3x3 kernel used in each. With relu activation in each.
We will give it all the images planes stacked into one image. We will ask it to activate one of four neurons to represent which corner it thinks the circle is hidding at the end.
"""
#Let's define a pretty straight forward conv neural network. It will have about 200K params, given the input image dimension of 100 x 100
import keras
from keras.layers import Flatten, Conv2D, Dense
img = keras.Input(shape=img_shape)
x = img
x = Conv2D(64, (3, 3), strides=(2, 2), activation="relu")(x)
x = Conv2D(64, (3, 3), strides=(2, 2), activation="relu")(x)
x = Conv2D(64, (3, 3), strides=(2, 2), activation="relu")(x)
x = Conv2D(64, (3, 3), strides=(2, 2), activation="relu")(x)
x = Flatten()(x)
x = Dense(100, activation='relu')(x)
x = Dense(num_corners, activation='softmax')(x)
model = keras.Model(img, x)
model.compile(optimizer="adam",
loss="categorical_crossentropy",
metrics=["acc"])
print(model.summary())
# using: CMSSW_10_2_15_patch2/external/slc7_amd64_gcc700/bin/root
# doc from: https://keras.io/guides/functional_api/
# non-linear connectivity topologies - w. layers that are not connected sequentially
# sth Sequential API can not handle.
import keras as kr
inputs = kr.Input(shape=(32, 32, 3), name="img")
x = kr.layers.Conv2D(32, 3, activation='relu')(inputs)
x = kr.layers.Conv2D(64, 3, activation='relu')(x)
block_1_output = kr.layers.MaxPooling2D(3)(x)
x = kr.layers.Conv2D(64, 3, activation='relu', padding="same")(block_1_output)
x = kr.layers.Conv2D(64, 3, activation='relu', padding="same")(x)
block_2_output = kr.layers.add([x, block_1_output])
x = kr.layers.Conv2D(64, 3, activation='relu', padding="same")(block_2_output)
x = kr.layers.Conv2D(64, 3, activation='relu', padding="same")(x)
block_3_output = kr.layers.add([x, block_2_output])
x = kr.layers.Conv2D(64, 3, activation='relu')(block_3_output)
x = kr.layers.GlobalAveragePooling2D()(x)
x = kr.layers.Dense(254, activation='relu')(x)
x = kr.layers.Dropout(0.5)(x)
outputs = kr.layers.Dense(10)(x)
model = kr.Model(inputs, outputs, name="toy_resnet")
model.summary()
# keras.utils.plot_model(model, "mini_resnet.png", show_shapes=True)
(x_train, y_train), (x_test, y_test) = kr.datasets.cifar10.load_data()
x_train = x_train.astype("float32") / 255.
def test_output_shape(self):
inputs = keras.Input(batch_size=16, shape=(4, ), dtype=tf.int64)
layer = discretization.Discretization(bin_boundaries=[-0.5, 0.5, 1.5])
outputs = layer(inputs)
self.assertAllEqual(outputs.shape.as_list(), [16, 4])
def lstm_cross_att_focal_multitask_model():
# DATA PIPELINING:
ds_series_train = tf.data.Dataset.from_generator(
data_gen_train_gender,
output_types=((tf.float64, {'accent': tf.float64, 'gender': tf.float32})),
output_shapes=(((1000, 83), {'accent': (8), 'gender': (1)})) )
ds_series_test = tf.data.Dataset.from_generator(
data_gen_test_gender,
output_types=((tf.float64, {'accent': tf.float64, 'gender': tf.float32})),
output_shapes=(((1000, 83), {'accent': (8), 'gender': (1)})) )
ds_series_val = tf.data.Dataset.from_generator(
data_gen_val_gender,
output_types=((tf.float64, {'accent': tf.float64, 'gender': tf.float32})),
output_shapes=(((1000, 83), {'accent': (8), 'gender': (1)})) )
print('Train Data: {}'.format(ds_series_train))
print('Test Data: {}'.format(ds_series_test))
print('Val Data: {}'.format(ds_series_val))
ds_series_train_batch = ds_series_train.shuffle(125555).padded_batch(64)
ds_series_test_batch = ds_series_test.shuffle(15000).padded_batch(64)
ds_series_val_batch = ds_series_val.shuffle(11988).padded_batch(64)
# MODEL ARCHITECTURE
inputs = keras.Input(shape=(1000, 83,))
lstm = LSTM(512, kernel_regularizer=regularizers.l1_l2(l1=0.00001, l2=0.00001), input_shape=(1000, 83), name = 'LSTM',
recurrent_regularizer=None, bias_regularizer=regularizers.l2(1e-4),
activity_regularizer=regularizers.l2(1e-5), kernel_constraint=None,
recurrent_constraint=None, bias_constraint=None, dropout=0.2, return_sequences=True)(inputs)
att, att_weight = attention()(lstm)
#reshaped = Reshape((512,))(K.sum(att,axis=1))
reshaped = Reshape((1512,))(tf.concat([K.sum(att,axis=1), K.sum(att,axis=2)], axis=1))
drop1 = keras.layers.Dropout(0.2)(reshaped)
dense = Dense(128, activation = 'sigmoid', kernel_regularizer=regularizers.l2(0.001), activity_regularizer=tf.keras.regularizers.l2(0.01))(drop1)
drop2 = keras.layers.Dropout(0.2)(dense)
output_1 = Dense(8, activation = 'softmax', name='accent', kernel_regularizer=regularizers.l2(0.001), activity_regularizer=tf.keras.regularizers.l2(0.01))(drop2)
output_2 = Dense(1, activation='relu', name = 'gender', kernel_regularizer=regularizers.l2(0.001), activity_regularizer=tf.keras.regularizers.l2(0.01))(drop2)
model = keras.Model(inputs=inputs, outputs=[output_1, output_2])
model.summary()
#plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)
model.compile(optimizer='adam', loss={'accent': focal_loss(gamma=5., alpha=0.5), 'gender': 'binary_crossentropy'}, metrics = {'accent': 'accuracy'},
loss_weights = [10, 0.2])
es = EarlyStopping(monitor='val_loss', mode='min', verbose=5, patience=20)
mc = ModelCheckpoint('../../lstm_cross_att_multitask_model_wts.h5', monitor='val_loss', mode='min', save_best_only=True, save_weights = True, verbose=1)
#tb_callback = tf.keras.callbacks.TensorBoard('./logs', update_freq = 1)
hist = model.fit(ds_series_train_batch, validation_data = ds_series_val_batch, epochs=100, callbacks=[es, mc])
############################ TESTING THE MODEL ######################################
print()
print()
print('TESTING for LSTM ATTENTION with CROSSENTROPY LOSS.....................................')
print()
model.load_weights('../../lstm_cross_att_multitask_model_wts.h5')
model.evaluate(ds_series_test_batch)
return model
def build_model(hidden, features, predict_n, look_back=10, batch_size=1):
"""
Builds and returns the LSTM model with the parameters given
:param hidden: number of hidden nodes
:param features: number of variables in the example table
:param look_back: Number of time-steps to look back before predicting
:param batch_size: batch size for batch training
:return:
"""
#batch_input_shape=(batch_size, look_back, features)
inp = keras.Input(shape=(look_back, features),
batch_shape=(batch_size, look_back, features))
x = LSTM(
hidden,
input_shape=(look_back, features),
stateful=True,
batch_input_shape=(batch_size, look_back, features),
return_sequences=True,
# activation='relu',
dropout=0.1,
recurrent_dropout=0.1,
implementation=2,
unit_forget_bias=True,
)(inp, training=True)
x = Dropout(0.2)(x, training=True)
x = LSTM(
hidden,
input_shape=(look_back, features),
stateful=True,
batch_input_shape=(batch_size, look_back, features),
return_sequences=True,
# activation='relu',
dropout=0.1,
recurrent_dropout=0.1,
implementation=2,
unit_forget_bias=True,
)(x, training=True)
x = Dropout(0.2)(x, training=True)
x = LSTM(
hidden,
input_shape=(look_back, features),
stateful=True,
batch_input_shape=(batch_size, look_back, features),
# activation='relu',
dropout=0.1,
recurrent_dropout=0.1,
implementation=2,
unit_forget_bias=True,
)(x, training=True)
x = Dropout(0.2)(x, training=True)
out = Dense(
predict_n,
activation="relu",
kernel_initializer="random_uniform",
bias_initializer="zeros",
)(x)
model = keras.Model(inp, out)
# model = Sequential()
#
# model.add(
# LSTM(
# hidden,
# input_shape=(look_back, features),
# stateful=True,
# batch_input_shape=(batch_size, look_back, features),
# return_sequences=True,
# # activation='relu',
# dropout=0,
# recurrent_dropout=0,
# implementation=2,
# unit_forget_bias=True,
# )
# )
# model.add(Dropout(0.2))
# model.add(
# LSTM(
# hidden,
# input_shape=(look_back, features),
# stateful=True,
# batch_input_shape=(batch_size, look_back, features),
# return_sequences=True,
# # activation='relu',
# dropout=0,
# recurrent_dropout=0,
# implementation=2,
# unit_forget_bias=True,
# )
# )
# model.add(Dropout(0.2))
# model.add(
# LSTM(
# hidden,
# input_shape=(look_back, features),
# stateful=True,
# batch_input_shape=(batch_size, look_back, features),
# # activation='relu',
# dropout=0,
# recurrent_dropout=0,
# implementation=2,
# unit_forget_bias=True,
# )
# )
# model.add(Dropout(0.2))
# model.add(
# Dense(
# predict_n,
# activation="relu",
# kernel_initializer="random_uniform",
# bias_initializer="zeros",
# )
# )
start = time()
model.compile(loss="msle",
optimizer="nadam",
metrics=["accuracy", "mape", "mse"])
print("Compilation Time : ", time() - start)
plot_model(model, to_file="../figures/LSTM_model.png")
print(model.summary())
return model
def _MTLSTMMixed_MatchSpace(X,
Y,
fit_model_wrapper,
T0=None,
K_fixed=0,
M_sizes=None,
dropout_rate=0.2,
epochs=2,
verbose=0,
hidden_length=100,
**kwargs):
# could have just used the LSTM state units direclty, but having this be big and then timeDistributed to narrow down is more expressive/powerful
with capture_all(): # doesn't have quiet option
import keras
if M_sizes is None:
M_sizes = range(1, 2 * int(np.log(Y.shape[0])))
if T0 is None:
T0 = X.shape[1]
if verbose == 0:
import os
if ("TF_CPP_MIN_LOG_LEVEL" in os.environ
and os.environ["TF_CPP_MIN_LOG_LEVEL"] != "2"
and os.environ["TF_CPP_MIN_LOG_LEVEL"] != "3"):
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
# Otherwise prints random info about CPU instruction sets
Cov_F, Cov_TV, Out_pre = _split_LSTM_x_data(X, T0, K_fixed=K_fixed)
LSTM_x = _shape_LSTM_x_data(Cov_F, Cov_TV, Out_pre)
LSTM_y = _shape_LSTM_y_data(Out_pre, Y, T0)
LSTM_K = LSTM_x.shape[2]
T1 = Y.shape[1]
# fits_single = {}
int_layer_single = {}
Vs_single = {}
scores = np.zeros((len(M_sizes)))
for i, M_size in enumerate(M_sizes):
inp = keras.Input(
batch_shape=(1, T0, LSTM_K), name="input"
) # batch_shape=(1,..) ensures trained on one case at time
with capture_all(): # doesn't have quiet option
x1 = keras.layers.LSTM(units=hidden_length,
return_sequences=True)(inp) ##
x2 = keras.layers.Dropout(rate=dropout_rate)(x1) ##
core = keras.layers.TimeDistributed(keras.layers.Dense(
units=M_size, activation="elu"),
name="embedding")(x2)
output_vec = []
for t in range(T1):
new_output = keras.layers.Dense(units=1,
activation="linear",
name="yp%s" % (t))(core)
output_vec.append(new_output)
model = keras.models.Model(inputs=inp, outputs=output_vec)
model.compile(loss="mse",
optimizer="Adam",
metrics=["mean_squared_error"])
model.fit(x=LSTM_x,
y=LSTM_y,
batch_size=1,
epochs=epochs,
verbose=verbose)
outputs_fit = model.get_layer(
name="embedding").output # .get_output_at(node_index = 1)
intermediate_layer_model = keras.models.Model(inputs=model.input,
outputs=outputs_fit)
final_weights = np.empty((T1, M_size))
n_layers_w = len(model.get_weights())
for t in range(T1):
l_i = n_layers_w - 2 - 2 * t
final_weights[t, :] = model.get_weights()[l_i][:, 0]
V_i = np.mean(np.abs(final_weights), axis=0)
transformer_i = LSTMTransformer(T0, K_fixed, intermediate_layer_model)
sc_fit_i = fit_model_wrapper(transformer_i, V_i)
# fits_single[i] = sc_fit_i
int_layer_single[i] = intermediate_layer_model
Vs_single[i] = V_i
scores[i] = sc_fit_i.score # = sc_fit_i.score_R2
i_best = np.argmin(scores)
best_M_size = M_sizes[i_best]
V_best = Vs_single[i_best]
intermediate_layer_model = int_layer_single[i_best]
transformer = LSTMTransformer(T0, K_fixed, intermediate_layer_model)
return transformer, V_best, best_M_size, V_best
def test_minimal_rnn_cell_layer():
class MinimalRNNCell(keras.layers.Layer):
def __init__(self, units, **kwargs):
self.units = units
self.state_size = units
super(MinimalRNNCell, self).__init__(**kwargs)
def build(self, input_shape):
self.kernel = self.add_weight(shape=(input_shape[-1], self.units),
initializer='uniform',
name='kernel')
self.recurrent_kernel = self.add_weight(shape=(self.units,
self.units),
initializer='uniform',
name='recurrent_kernel')
self.built = True
def call(self, inputs, states):
prev_output = states[0]
h = keras.backend.dot(inputs, self.kernel)
output = h + keras.backend.dot(prev_output, self.recurrent_kernel)
return output, [output]
def get_config(self):
config = {'units': self.units}
base_config = super(MinimalRNNCell, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
# Test basic case.
x = keras.Input((None, 5))
cell = MinimalRNNCell(32)
layer = recurrent.RNN(cell)
y = layer(x)
model = keras.models.Model(x, y)
model.compile(optimizer='rmsprop', loss='mse')
model.train_on_batch(np.zeros((6, 5, 5)), np.zeros((6, 32)))
# Test basic case serialization.
x_np = np.random.random((6, 5, 5))
y_np = model.predict(x_np)
weights = model.get_weights()
config = layer.get_config()
with keras.utils.CustomObjectScope({'MinimalRNNCell': MinimalRNNCell}):
layer = recurrent.RNN.from_config(config)
y = layer(x)
model = keras.models.Model(x, y)
model.set_weights(weights)
y_np_2 = model.predict(x_np)
assert_allclose(y_np, y_np_2, atol=1e-4)
# Test stacking.
cells = [MinimalRNNCell(8), MinimalRNNCell(12), MinimalRNNCell(32)]
layer = recurrent.RNN(cells)
y = layer(x)
model = keras.models.Model(x, y)
model.compile(optimizer='rmsprop', loss='mse')
model.train_on_batch(np.zeros((6, 5, 5)), np.zeros((6, 32)))
# Test stacked RNN serialization.
x_np = np.random.random((6, 5, 5))
y_np = model.predict(x_np)
weights = model.get_weights()
config = layer.get_config()
with keras.utils.CustomObjectScope({'MinimalRNNCell': MinimalRNNCell}):
layer = recurrent.RNN.from_config(config)
y = layer(x)
model = keras.models.Model(x, y)
model.set_weights(weights)
y_np_2 = model.predict(x_np)
assert_allclose(y_np, y_np_2, atol=1e-4)
def __init__(self, batch_size):
#self.khaiii = KhaiiiApi()
# LSTM Input : (795 + 792)*1
# LSTM Output : (1000)
# LSTM Output with time series : (1000 (Feature) * 10 (times))
# discriminator Input : 1000 * 10
# discriminator Output : 1 (0 or 1)
self.batch_size = batch_size
self.pv_size = 792
self.stock_size = 795
self.gen_output = 636
self.gen_timestep = 10
#self.scaler = MinMaxScaler()
self.article = {
"host": '127.0.0.1',
"port": 3306,
"user": '******',
"password": '******',
"db": 'mydb',
'charset': 'utf8'
}
self.index = {
"host": '127.0.0.1',
"port": 3306,
"user": '******',
"password": '******',
"db": 'mydb',
'charset': 'utf8'
}
self.gen_feature = self.pv_size + self.stock_size
self.dis_input = self.gen_output * self.gen_timestep
self.dis_output = 1
print("Start Building Data")
self.GAN_trainY = pickle.load(open('v4_trainY.sav', 'rb'))
GAN_trainX_STOCK = pickle.load(open('v4_trainX_STOCK.sav', 'rb'))
GAN_trainX_PV = pickle.load(open('trainX_PV.sav', 'rb'))
self.GAN_testY = pickle.load(open('v4_testY.sav', 'rb'))
GAN_testX_STOCK = pickle.load(open('v4_testX_STOCK.sav', 'rb'))
GAN_testX_PV = pickle.load(open('testX_PV.sav', 'rb'))
GAN_trainX_PV = np.delete(GAN_trainX_PV, (0), axis=0)
GAN_testX_PV = np.delete(GAN_testX_PV, (0), axis=0)
self.GAN_trainX = np.concatenate((GAN_trainX_STOCK, GAN_trainX_PV),
axis=2)
self.GAN_testX = np.concatenate((GAN_testX_STOCK, GAN_testX_PV),
axis=2)
self.GAN_trainSTOCK = pickle.load(open('v4_trainSTOCK.sav', 'rb'))
self.GAN_testSTOCK = pickle.load(open('v4_testSTOCK.sav', 'rb'))
print("Training Data Processing Finished")
self.discriminator = self.build_discriminator()
self.generator = self.build_generator(LayerName='LSTM')
#self.generator.compile(loss=root_mean_squared_error,
#optimizer=keras.optimizers.Adam(0.00002, 0.5),metrics=['accuracy'])
self.discriminator.compile(loss='binary_crossentropy',
optimizer=keras.optimizers.Adam(
0.000001, 0.5),
metrics=['accuracy'])
self.discriminator.trainable = False
combined_input = keras.Input(shape=((self.gen_timestep),
self.gen_feature),
name='stock_news_input')
#combined = 기사 + 주식
gen_stock = self.generator(inputs=combined_input)
#gen_stock : 795 차원의 10일 뒤 결과
past_stock = keras.Input(shape=((self.gen_timestep), self.gen_output),
name='past_stock')
#past_stock : 과거 9일간의 데이터
combined_stock = keras.layers.concatenate(
inputs=[past_stock, gen_stock], axis=1, name='combined_stock')
#combined_stock : 총 10일간의 데이터 past + gen_stock
valid = self.discriminator(inputs=combined_stock)
self.combined = keras.Model(inputs=[combined_input, past_stock],
outputs=valid)
self.combined.compile(loss='binary_crossentropy',
optimizer=keras.optimizers.Adam(0.000001, 0.5),
metrics=['accuracy'])
self.combined.summary()
def test_add_dynamic_shape(self): i1 = keras.Input(batch_shape=(4, None), dtype='float32') i2 = keras.Input(batch_shape=(4, 5), dtype='float32') layer = keras.layers.Add() o = layer([i1, i2]) self.assertListEqual(o.shape.as_list(), [4, 5])
def test_unspecified_weight_dim_fails(self):
input_tensor = keras.Input(shape=(32, ))
layer = einsum_dense.EinsumDense(equation="ab,zd->ad", output_shape=64)
with self.assertRaisesRegex(
ValueError, ".*Weight dimension 'z' did not have a match "):
_ = layer(input_tensor)
def input_nub_text_handler(variable, input_dataframe):
"""
Create an input nub for text data, by:
- Finding all derived variables. With a variable `name` and sequence length of 4, there would be 4 derived
variables, `name_0` through `name_4`
- Creating an appropriately shaped input layer, embedding layer, and all future layers
- Return both the input layer and last layer (both are necessary for creating a model)
:param variable: Name of the variable
:type variable: str
:param input_dataframe: A dataframe, containing either the specified variable, or derived variables
:type input_dataframe: pandas.DataFrame
:return: A tuple containing the input layer, and the last layer of the nub
"""
logging.info('Creating text input nub for variable: {}'.format(variable))
# Get transformed data for shaping
if variable in input_dataframe.columns:
variable_list = [variable]
else:
variable_name_prefix = variable + '_'
variable_list = list(
filter(lambda x: x.startswith(variable_name_prefix),
input_dataframe.columns))
logging.info('Text var has variable / derived variable list: {}'.format(
variable_list))
transformed = input_dataframe[variable_list].as_matrix()
# Set up sequence length for input_layer
if len(transformed.shape) >= 2:
input_sequence_length = int(transformed.shape[1])
else:
input_sequence_length = 1
# Get the vocab size (number of rows in the embedding). The additional offsets are due to 1 for len vs indexing w/
# 0, 1 for unknown token, and the others for something else?
vocab_size = int(numpy.max(transformed)) + 4
# Determine the embedding output size (number of columns in the embedding)
# TODO There must be a better heuristic
embedding_output_dim = 200
logging.info(
'Creating embedding for text_var: {}, with input_sequence_length: {}, vocab size: {}, '
'and embedding_output_dim: {}'.format(variable, input_sequence_length,
vocab_size,
embedding_output_dim))
# Create & stack layers
input_layer = keras.Input(shape=(input_sequence_length, ),
name='input_{}'.format(variable))
embedding_layer = Embedding(input_dim=vocab_size,
output_dim=embedding_output_dim,
input_length=input_sequence_length,
name='embedding_{}'.format(variable))
x = embedding_layer(input_layer)
x = Bidirectional(LSTM(128, name='lstm_{}'.format(variable)),
name='bidirectiona_lstm_{}'.format(variable))(x)
return input_layer, x
new_df['font_size'] = df.font_size / df.font_size.max()
new_df['chars'] = df.chars / df.chars.max()
new_df['lines'] = df.lines / df.lines.max()
return new_df
# In[107]:
keras_ready = convert_to_padded_seqs(scale_df(df))
# In[115]:
inp = keras.Input((None, 6))
rnn = LSTM(16, activation='tanh', dropout=0.5, return_sequences=True)(inp)
dense1 = TimeDistributed(Dense(3, activation='linear'))(rnn)
dense2 = TimeDistributed(Dense(3, activation='relu'))(rnn)
output = Concatenate()([dense1, dense2])
model = keras.models.Model(input=inp, output=output)
model.compile(loss='mean_squared_error',
optimizer='adam')
# In[129]:
X = keras_ready[:,:-1,:]
Y = keras_ready[:,1:,:]
h = model.fit(x=X, y=Y, batch_size=1, epochs=10)
numpy.random.seed(seed)
# load dataset
dataframe = read_csv(
"https://archive.ics.uci.edu/ml/machine-learning-databases/undocumented/connectionist-bench/sonar/sonar.all-data ",
header=None)
dataset = dataframe.values
# split into input (X) and output (Y) variables
X = dataset[:, 0:60].astype(float)
Y = dataset[:, 60]
# encode class values as integers
dataframe[60] = [0 if x == 'R' else 1 for x in dataframe[60]]
encoded_Y = dataframe[60]
Y = to_categorical(encoded_Y)
inputs = keras.Input(shape=(60, ))
x = Dense(60, activation='relu')(inputs)
x = Dense(30, activation='relu')(x)
outputs = Dense(2, activation='sigmoid')(x)
#creating model
model = keras.Model(inputs, outputs)
# Compile model
sgd = SGD(lr=0.01, momentum=0.8, decay=0.0, nesterov=False)
model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])
model.fit(X, Y, epochs=300)
# Smaller Network
def test_rnn_cell_with_constants_layer_passing_initial_state():
class RNNCellWithConstants(keras.layers.Layer):
def __init__(self, units, **kwargs):
self.units = units
self.state_size = units
super(RNNCellWithConstants, self).__init__(**kwargs)
def build(self, input_shape):
if not isinstance(input_shape, list):
raise TypeError('expects constants shape')
[input_shape, constant_shape] = input_shape
# will (and should) raise if more than one constant passed
self.input_kernel = self.add_weight(shape=(input_shape[-1],
self.units),
initializer='uniform',
name='kernel')
self.recurrent_kernel = self.add_weight(shape=(self.units,
self.units),
initializer='uniform',
name='recurrent_kernel')
self.constant_kernel = self.add_weight(shape=(constant_shape[-1],
self.units),
initializer='uniform',
name='constant_kernel')
self.built = True
def call(self, inputs, states, constants):
[prev_output] = states
[constant] = constants
h_input = keras.backend.dot(inputs, self.input_kernel)
h_state = keras.backend.dot(prev_output, self.recurrent_kernel)
h_const = keras.backend.dot(constant, self.constant_kernel)
output = h_input + h_state + h_const
return output, [output]
def get_config(self):
config = {'units': self.units}
base_config = super(RNNCellWithConstants, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
# Test basic case.
x = keras.Input((None, 5))
c = keras.Input((3, ))
s = keras.Input((32, ))
cell = RNNCellWithConstants(32)
layer = recurrent.RNN(cell)
y = layer(x, initial_state=s, constants=c)
model = keras.models.Model([x, s, c], y)
model.compile(optimizer='rmsprop', loss='mse')
model.train_on_batch(
[np.zeros((6, 5, 5)),
np.zeros((6, 32)),
np.zeros((6, 3))], np.zeros((6, 32)))
# Test basic case serialization.
x_np = np.random.random((6, 5, 5))
s_np = np.random.random((6, 32))
c_np = np.random.random((6, 3))
y_np = model.predict([x_np, s_np, c_np])
weights = model.get_weights()
config = layer.get_config()
custom_objects = {'RNNCellWithConstants': RNNCellWithConstants}
with keras.utils.CustomObjectScope(custom_objects):
layer = recurrent.RNN.from_config(config.copy())
y = layer(x, initial_state=s, constants=c)
model = keras.models.Model([x, s, c], y)
model.set_weights(weights)
y_np_2 = model.predict([x_np, s_np, c_np])
assert_allclose(y_np, y_np_2, atol=1e-4)
# verify that state is used
y_np_2_different_s = model.predict([x_np, s_np + 10., c_np])
with pytest.raises(AssertionError):
assert_allclose(y_np, y_np_2_different_s, atol=1e-4)
# test flat list inputs
with keras.utils.CustomObjectScope(custom_objects):
layer = recurrent.RNN.from_config(config.copy())
y = layer([x, s, c])
model = keras.models.Model([x, s, c], y)
model.set_weights(weights)
y_np_3 = model.predict([x_np, s_np, c_np])
assert_allclose(y_np, y_np_3, atol=1e-4)
def createModel(train_data, val_data, train_labels, val_labels, test_data):
mean = np.mean(train_data, axis=0)
std = np.std(train_data, axis=0)
print(mean)
std_train_data = (train_data - mean) / std
std_val_data = (val_data - mean) / std
std_test_data = (test_data - mean) / std
input_layer = keras.Input(shape=(number_of_features, ))
hidden_layer_1 = submodel(input_layer, name="1")
encoded = layers.Dense(encoding_dim, activation='relu',
name="2_1")(hidden_layer_1)
hidden_layer_2 = layers.BatchNormalization(name="2_2")(encoded)
hidden_layer_2 = layers.Dropout(0.5, name="2_3")(hidden_layer_2)
hidden_layer_3_1 = submodel(hidden_layer_2, name="3")
hidden_layer_3_2 = submodel(hidden_layer_2, name="4")
reg = layers.Dense(1, name="reg_out")(hidden_layer_3_1)
decoded = layers.Dense(number_of_features,
name="dec_out")(hidden_layer_3_2)
model = keras.Model(inputs=input_layer, outputs=[reg, decoded])
model.compile(loss={
'reg_out': 'mean_squared_error',
'dec_out': 'mean_squared_error'
},
optimizer=Adam(lr=0.001, decay=0.001 / epochs))
history = model.fit(std_train_data, {
'reg_out': train_labels,
'dec_out': std_train_data
},
epochs=epochs,
batch_size=16,
shuffle=True,
validation_data=(std_val_data, {
'reg_out': val_labels,
'dec_out': std_val_data
}))
encoder = keras.Model(input_layer, encoded)
regress_input = keras.Input(shape=(encoding_dim, ))
hidden_layer = layers.BatchNormalization()(regress_input)
hidden_layer = layers.Dropout(0.5)(hidden_layer)
hidden_layer = layers.Dense(number_of_neurons,
activation='relu')(hidden_layer)
hidden_layer = layers.BatchNormalization()(hidden_layer)
hidden_layer = layers.Dropout(0.5)(hidden_layer)
regress_output = layers.Dense(1)(hidden_layer)
regression = keras.Model(regress_input, regress_output)
regression.layers[1].set_weights(model.get_layer("2_2").get_weights())
regression.layers[2].set_weights(model.get_layer("2_3").get_weights())
regression.layers[3].set_weights(model.get_layer("3_1").get_weights())
regression.layers[4].set_weights(model.get_layer("3_2").get_weights())
regression.layers[5].set_weights(model.get_layer("3_3").get_weights())
regression.layers[6].set_weights(model.get_layer("reg_out").get_weights())
decoder_input = keras.Input(shape=(encoding_dim, ))
hidden_layer = layers.BatchNormalization()(decoder_input)
hidden_layer = layers.Dropout(0.5)(hidden_layer)
hidden_layer = layers.Dense(number_of_neurons,
activation='relu')(hidden_layer)
hidden_layer = layers.BatchNormalization()(hidden_layer)
hidden_layer = layers.Dropout(0.5)(hidden_layer)
decoder_output = layers.Dense(number_of_features)(hidden_layer)
decoder = keras.Model(decoder_input, decoder_output)
decoder.layers[1].set_weights(model.get_layer("2_2").get_weights())
decoder.layers[2].set_weights(model.get_layer("2_3").get_weights())
decoder.layers[3].set_weights(model.get_layer("4_1").get_weights())
decoder.layers[4].set_weights(model.get_layer("4_2").get_weights())
decoder.layers[5].set_weights(model.get_layer("4_3").get_weights())
decoder.layers[6].set_weights(model.get_layer("dec_out").get_weights())
encoder.save("encoder.h5")
regression.save("regression.h5")
decoder.save("decoder.h5")
encods = encodeData(std_test_data, encoder)
decodeData(encods, decoder, mean, std)
computeRegression(encods, regression)
def test_shared_objects(self):
class OuterLayer(keras.layers.Layer):
def __init__(self, inner_layer):
super(OuterLayer, self).__init__()
self.inner_layer = inner_layer
def call(self, inputs):
return self.inner_layer(inputs)
def get_config(self):
return {
'inner_layer':
generic_utils.serialize_keras_object(self.inner_layer)
}
@classmethod
def from_config(cls, config):
return cls(
generic_utils.deserialize_keras_object(
config['inner_layer']))
class InnerLayer(keras.layers.Layer):
def __init__(self):
super(InnerLayer, self).__init__()
self.v = self.add_weight(name='v', shape=[], dtype=tf.float32)
def call(self, inputs):
return self.v + inputs
@classmethod
def from_config(cls, config):
return cls()
# Create a model with 2 output layers that share the same inner layer.
inner_layer = InnerLayer()
outer_layer_1 = OuterLayer(inner_layer)
outer_layer_2 = OuterLayer(inner_layer)
input_ = keras.Input(shape=(1, ))
model = keras.Model(
inputs=input_,
outputs=[outer_layer_1(input_),
outer_layer_2(input_)])
# Changes to the shared layer should affect both outputs.
model.layers[1].inner_layer.v.assign(5)
self.assertAllEqual(model(1), [6.0, 6.0])
model.layers[1].inner_layer.v.assign(3)
self.assertAllEqual(model(1), [4.0, 4.0])
# After loading, changes to the shared layer should still affect both
# outputs.
def _do_assertions(loaded):
loaded.layers[1].inner_layer.v.assign(5)
self.assertAllEqual(loaded(1), [6.0, 6.0])
loaded.layers[1].inner_layer.v.assign(3)
self.assertAllEqual(loaded(1), [4.0, 4.0])
loaded.layers[2].inner_layer.v.assign(5)
self.assertAllEqual(loaded(1), [6.0, 6.0])
loaded.layers[2].inner_layer.v.assign(3)
self.assertAllEqual(loaded(1), [4.0, 4.0])
# We'd like to make sure we only attach shared object IDs when strictly
# necessary, so we'll recursively traverse the generated config to count
# whether we have the exact number we expect.
def _get_all_keys_recursive(dict_or_iterable):
if isinstance(dict_or_iterable, dict):
for key in dict_or_iterable.keys():
yield key
for key in _get_all_keys_recursive(dict_or_iterable.values()):
yield key
elif isinstance(dict_or_iterable, string_types):
return
else:
try:
for item in dict_or_iterable:
for key in _get_all_keys_recursive(item):
yield key
# Not an iterable or dictionary
except TypeError:
return
with generic_utils.CustomObjectScope({
'OuterLayer': OuterLayer,
'InnerLayer': InnerLayer
}):
# Test saving and loading to disk
save_format = testing_utils.get_save_format()
saved_model_dir = self._save_model_dir()
keras.models.save_model(model,
saved_model_dir,
save_format=save_format)
loaded = keras.models.load_model(saved_model_dir)
_do_assertions(loaded)
# Test recreating directly from config
config = model.get_config()
key_count = collections.Counter(_get_all_keys_recursive(config))
self.assertEqual(key_count[generic_utils.SHARED_OBJECT_KEY], 2)
loaded = keras.Model.from_config(config)
_do_assertions(loaded)
) # 一共6个变量，每个变量6列：t-5,t-4,t-3,t-2,t-1,t;shape应为(原长 - step_size)*((step_size+1)*fea_num)
# split into train and test sets
values = reframed.values
n_train_days = 61 - 1
train = values[:n_train_days, :]
test = values[n_train_days:, :]
return train, test, scaler
step_size = 5
feature_num = 6
generator_input = keras.Input(shape=(step_size, feature_num))
x = layers.LSTM(75, return_sequences=True)(generator_input)
#x = layers.Dropout(0.2)(x)
x = layers.LSTM(25)(x)
x = layers.Dense(1)(x)
x = layers.LeakyReLU()(x)
generator = keras.models.Model(generator_input, x)
generator.summary()
discriminator_input = layers.Input(shape=(step_size + 1, 1))
y = layers.Dense(72)(discriminator_input)
y = layers.LeakyReLU(alpha=0.05)(y)
y = layers.Dense(100)(y)
y = layers.LeakyReLU(alpha=0.05)(y)
y = layers.Dense(10)(y)
y = layers.LeakyReLU(alpha=0.05)(y)
def _make_graph_network(input_size, output_size):
inputs = keras.Input(input_size)
x = keras.layers.Dense(8, activation='relu')(inputs)
y = keras.layers.Dense(output_size)(x)
return keras.Model(inputs=inputs, outputs=y)
from skimage.metrics import structural_similarity as ssim from enum import auto import tensorflow as tf import matplotlib.pyplot as plt import numpy as np from keras.datasets import mnist from json import detect_encoding import keras from keras import layers from tensorflow.python.keras.backend import shape from tensorflow.keras import layers, losses from keras.callbacks import TensorBoard #tensorboard --logdir=/tmp/autoencoder input_img = keras.Input(shape=(28, 28, 1)) x = layers.Conv2D(16, (3, 3), activation='relu', padding='same')(input_img) x = layers.MaxPool2D((2, 2), padding='same')(x) x = layers.Conv2D(8, (3, 3), activation='relu', padding='same')(x) x = layers.MaxPool2D((2, 2), padding='same')(x) x = layers.Conv2D(8, (3, 3), activation='relu', padding='same')(x) encoded = layers.MaxPooling2D((2, 2), padding='same')(x) x = layers.Conv2D(8, (3, 3), activation='relu', padding='same')(encoded) x = layers.UpSampling2D((2, 2))(x) x = layers.Conv2D(8, (3, 3), activation='relu', padding='same')(x) x = layers.UpSampling2D((2, 2))(x)
def testRecompileWithChangingProgramCacheSize(self):
# This test is thanks to iperov,
# who reported https://github.com/plaidml/plaidml/issues/274,
# demonstrating a case where exceeding certain number of ops
# causes recompiling of kernels (the encoder is slightly modified from
# his example for reproduciblilty)
shape = (64, 64, 3)
LeakyReLU = keras.layers.LeakyReLU
def encflow(x):
x = LeakyReLU()(keras.layers.Conv2D(128,
5,
strides=2,
padding="same")(x))
x = keras.layers.Conv2D(128, 5, strides=2, padding="same")(x)
x = keras.layers.Conv2D(256, 5, strides=2, padding="same")(x)
x = keras.layers.Conv2D(256, 5, strides=2, padding="same")(x)
x = keras.layers.Conv2D(256, 5, strides=2, padding="same")(x)
x = keras.layers.Conv2D(512, 5, strides=2, padding="same")(x)
x = keras.layers.Conv2D(512, 5, strides=2, padding="same")(x)
x = keras.layers.Conv2D(1024, 5, strides=2, padding="same")(x)
x = keras.layers.Conv2D(1024, 5, strides=2, padding="same")(x)
x = keras.layers.Conv2D(1024, 5, strides=2, padding="same")(x)
x = keras.layers.Dense(64)(keras.layers.Flatten()(x))
x = keras.layers.Dense(4 * 4 * 1024)(x)
x = keras.layers.Reshape((4, 4, 1024))(x)
x = keras.layers.Conv2DTranspose(512, 3, strides=2,
padding="same")(x)
return x
def decflow(x):
x = x[0]
x = LeakyReLU()(keras.layers.Conv2DTranspose(512,
3,
strides=2,
padding="same")(x))
x = keras.layers.Conv2DTranspose(256, 3, strides=2,
padding="same")(x)
x = keras.layers.Conv2DTranspose(128, 3, strides=2,
padding="same")(x)
x = keras.layers.Conv2D(3, 5, strides=1, padding="same")(x)
return x
def modelify(model_functor):
def func(tensor):
return keras.models.Model(tensor, model_functor(tensor))
return func
encoder = modelify(encflow)(keras.Input(shape))
decoder1 = modelify(decflow)(
[keras.Input(pkb.int_shape(x)[1:]) for x in encoder.outputs])
decoder2 = modelify(decflow)(
[keras.Input(pkb.int_shape(x)[1:]) for x in encoder.outputs])
inp = x = keras.Input(shape)
code = encoder(x)
x1 = decoder1(code)
x2 = decoder2(code)
loss = pkb.mean(pkb.square(inp - x1)) + pkb.mean(pkb.square(inp - x2))
train_func = pkb.function(
[inp], [loss],
keras.optimizers.Adam().get_updates(
loss, encoder.trainable_weights + decoder1.trainable_weights +
decoder2.trainable_weights))
view_func1 = pkb.function([inp], [x1])
view_func2 = pkb.function([inp], [x2])
for i in range(5):
print("Loop %i" % i, flush=True)
data = np.zeros((1, 64, 64, 3))
train_func([data])
view_func1([data])
view_func2([data])
print("Saving weights", flush=True)
encoder.save_weights(r"testweights.h5")
decoder1.save_weights(r"testweights1.h5")
decoder2.save_weights(r"testweights2.h5")
def fer_vgg(input_shape=(48, 48, 1), input_classes=7):
inputs = keras.Input(shape=input_shape)
# Conv Block 1
x = Conv2D(64, (3, 3), activation='relu', padding='same')(inputs)
x = Dropout(0.25)(x, training=True)
x = Conv2D(64, (3, 3), activation='relu', padding='same')(inputs)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(x)
x = Dropout(0.25)(x, training=True)
# Conv Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)
x = Dropout(0.25)(x, training=True)
x = Conv2D(128, (3, 3),
activation='relu',
padding='same',
kernel_regularizer=l2(0.001))(x)
x = MaxPooling2D((2, 2), strides=(2, 2))(x)
x = Dropout(0.25)(x, training=True)
# Conv Block 3
x = Conv2D(256, (3, 3), padding='same', activation='relu')(x)
x = Dropout(0.25)(x, training=True)
x = Conv2D(256, (3, 3),
padding='same',
activation='relu',
kernel_regularizer=l2(0.001))(x)
x = Dropout(0.25)(x, training=True)
x = Conv2D(256, (3, 3),
padding='same',
activation='relu',
kernel_regularizer=l2(0.001))(x)
x = AveragePooling2D(pool_size=(2, 2), strides=(2, 2))(x)
x = Dropout(0.25)(x, training=True)
# Conv Block 4
x = Conv2D(512, (3, 3), padding='same', activation='relu')(x)
x = Dropout(0.25)(x, training=True)
x = Conv2D(512, (3, 3), padding='same', activation='relu')(x)
x = Dropout(0.25)(x, training=True)
x = Conv2D(512, (3, 3),
padding='same',
activation='relu',
kernel_regularizer=l2(0.001))(x)
x = Dropout(0.25)(x, training=True)
x = Conv2D(512, (3, 3),
padding='same',
activation='relu',
kernel_regularizer=l2(0.001))(x)
x = AveragePooling2D(pool_size=(2, 2), strides=(2, 2))(x)
x = Dropout(0.25)(x, training=True)
# Conv Block 5
x = Conv2D(512, (3, 3), padding='same', activation='relu')(x)
x = Dropout(0.25)(x, training=True)
x = Conv2D(512, (3, 3), padding='same', activation='relu')(x)
x = Dropout(0.25)(x, training=True)
x = Conv2D(512, (3, 3),
padding='same',
activation='relu',
kernel_regularizer=l2(0.001))(x)
x = Dropout(0.25)(x, training=True)
x = Conv2D(512, (3, 3),
padding='same',
activation='relu',
kernel_regularizer=l2(0.001))(x)
x = AveragePooling2D(pool_size=(2, 2), strides=(2, 2))(x)
x = Dropout(0.25)(x, training=True)
# FC Layers
x = Flatten()(x)
#x = Dense(4096, activation='relu')(x)
#x = Dropout(0.25)(x, training = True)
x = Dense(512, activation='relu')(x)
x = Dropout(0.25)(x, training=True)
x = Dense(256, activation='relu')(x)
x = Dropout(0.25)(x)
output = Dense(7, activation='softmax')(x)
model = keras.Model(inputs, output)
return model
def fluoro_model(X_talos, y_talos, X_val, y_val, params):
def root_mean_squared_error(y_true, y_pred):
return keras.backend.sqrt(
keras.backend.mean(keras.backend.square(y_pred - y_true)))
channel_order = 'channels_last'
img_input_shape = (128, 128, 1)
# Hyperparameters
# regularizer = keras.regularizers.l1_l2(l1 = params['regularizer_l1'], l2 = params['regularizer_l2'])
regularizer = keras.regularizers.l1_l2(l1=0.05, l2=0.2)
# activation_fn = params['activation_fn']
# kern_init = params['kern_init']
activation_fn = 'elu'
kern_init = 'glorot_uniform'
# conv_1_filters = params['conv_1_filters']
# conv_1_kernel = (params['conv_1_kernel'],params['conv_1_kernel'])
# conv_1_strides = (params['conv_1_strides'],params['conv_1_strides'])
conv_1_padding = 'valid'
conv_1_filters = 50
conv_1_kernel = (10, 10)
conv_1_strides = (2, 2)
# spatial_drop_rate_1 = params['spatial_drop_rate_1']
spatial_drop_rate_1 = 0.3
pool_1_size = (params['pool_1_size'], params['pool_1_size'])
pool_1_padding = 'same'
# pool_1_size = (2,2)
conv_2_filters = params['conv_2_filters']
conv_2_kernel = (params['conv_2_kernel'], params['conv_2_kernel'])
conv_2_strides = (params['conv_2_strides'], params['conv_2_strides'])
conv_2_padding = 'same'
# conv_2_filters = 80
# conv_2_kernel = (3,3)
# conv_2_strides = (1,1)
pool_2_size = (params['pool_2_size'], params['pool_2_size'])
pool_2_padding = 'same'
# pool_2_size = (2,2)
# conv_3_filters = params['conv_3_filters']
# conv_3_kernel = (params['conv_3_kernel'],params['conv_3_kernel'])
# conv_3_strides = (params['conv_3_strides'],params['conv_3_strides'])
conv_3_padding = 'valid'
conv_3_filters = 80
conv_3_kernel = (2, 2)
conv_3_strides = (1, 1)
pool_3_size = (2, 2)
pool_3_padding = 'valid'
# dense_1_f_units = params['dense_1_f_units']
dense_1_f_bias = True
dense_1_f_units = 80
# dense_2_f_units = params['dense_2_f_units']
dense_2_f_units = 120
dense_2_f_bias = True
# dense_3_f_units = params['dense_3_f_units']
dense_3_f_units = 120
dense_3_f_bias = True
# dense_1_ca_units = params['dense_1_ca_units']
dense_1_ca_units = 60
dense_1_ca_bias = True
# dense_2_co_units = params['dense_2_co_units']
dense_2_co_units = 80
dense_2_co_bias = True
# drop_1_comb_rate = params['drop_1_comb_rate']
drop_1_comb_rate = 0.1
# dense_3_co_units = params['dense_3_co_units']
dense_3_co_units = 80
dense_3_co_bias = True
main_output_units = 6
main_output_act = 'linear'
# model_opt = params['model_opt'](lr=params('learning_rate'))
model_opt = 'adam'
model_loss = 'mse'
model_metric = root_mean_squared_error
# model_epochs = params['model_epochs']
# model_batchsize = params['model_batchsize']
model_epochs = 30
model_batchsize = 10
input_fluoro_1 = keras.Input(shape=img_input_shape,
dtype='float32',
name='fluoro1_inpt')
input_fluoro_2 = keras.Input(shape=img_input_shape,
dtype='float32',
name='fluoro2_inpt')
input_cali = keras.Input(shape=(6, ), dtype='float32', name='cali_inpt')
bn_1_1 = keras.layers.BatchNormalization()(input_fluoro_1)
conv_1_1 = keras.layers.Conv2D(filters=conv_1_filters,
kernel_size=conv_1_kernel,
strides=conv_1_strides,
padding=conv_1_padding,
activation=activation_fn,
input_shape=img_input_shape,
data_format=channel_order,
activity_regularizer=regularizer,
kernel_initializer=kern_init)(bn_1_1)
spat_1_1 = keras.layers.SpatialDropout2D(
rate=spatial_drop_rate_1)(conv_1_1)
pool_1_1 = keras.layers.MaxPooling2D(pool_size=pool_1_size,
padding=pool_1_padding,
data_format=channel_order)(spat_1_1)
conv_2_1 = keras.layers.Conv2D(filters=conv_2_filters,
kernel_size=conv_2_kernel,
strides=conv_2_strides,
padding=conv_2_padding,
activation=activation_fn,
data_format=channel_order,
activity_regularizer=regularizer,
kernel_initializer=kern_init)(pool_1_1)
pool_2_1 = keras.layers.MaxPooling2D(pool_size=pool_2_size,
padding=pool_2_padding,
data_format=channel_order)(conv_2_1)
conv_3_1 = keras.layers.Conv2D(filters=conv_3_filters,
kernel_size=conv_3_kernel,
strides=conv_3_strides,
padding=conv_3_padding,
data_format=channel_order,
activity_regularizer=regularizer,
kernel_initializer=kern_init)(pool_2_1)
pool_3_1 = keras.layers.MaxPooling2D(pool_size=pool_3_size,
padding=pool_3_padding,
data_format=channel_order)(conv_3_1)
flatten_1_1 = keras.layers.Flatten()(pool_3_1)
dense_1_f_1 = keras.layers.Dense(units=dense_1_f_units,
activation=activation_fn,
use_bias=dense_1_f_bias,
kernel_initializer=kern_init,
activity_regularizer=regularizer,
name='dense_1_f_1')(flatten_1_1)
dense_2_f_1 = keras.layers.Dense(units=dense_2_f_units,
activation=activation_fn,
use_bias=dense_2_f_bias,
kernel_initializer=kern_init,
activity_regularizer=regularizer,
name='dense_2_f_1')(dense_1_f_1)
dense_3_f_1 = keras.layers.Dense(units=dense_3_f_units,
activation=activation_fn,
use_bias=dense_3_f_bias,
kernel_initializer=kern_init,
activity_regularizer=regularizer,
name='dense_3_f_1')(dense_2_f_1)
bn_1_2 = keras.layers.BatchNormalization()(input_fluoro_2)
conv_1_2 = keras.layers.Conv2D(filters=conv_1_filters,
kernel_size=conv_1_kernel,
strides=conv_1_strides,
padding=conv_1_padding,
activation=activation_fn,
input_shape=img_input_shape,
data_format=channel_order,
activity_regularizer=regularizer,
kernel_initializer=kern_init)(bn_1_2)
spat_1_2 = keras.layers.SpatialDropout2D(
rate=spatial_drop_rate_1)(conv_1_2)
pool_1_2 = keras.layers.MaxPooling2D(pool_size=pool_1_size,
padding=pool_1_padding,
data_format=channel_order)(spat_1_2)
conv_2_2 = keras.layers.Conv2D(filters=conv_2_filters,
kernel_size=conv_2_kernel,
strides=conv_2_strides,
padding=conv_2_padding,
activation=activation_fn,
data_format=channel_order,
activity_regularizer=regularizer,
kernel_initializer=kern_init)(pool_1_2)
pool_2_2 = keras.layers.MaxPooling2D(pool_size=pool_2_size,
padding=pool_2_padding,
data_format=channel_order)(conv_2_2)
conv_3_2 = keras.layers.Conv2D(filters=conv_3_filters,
kernel_size=conv_3_kernel,
strides=conv_3_strides,
padding=conv_3_padding,
data_format=channel_order,
activity_regularizer=regularizer,
kernel_initializer=kern_init)(pool_2_2)
pool_3_2 = keras.layers.MaxPooling2D(pool_size=pool_3_size,
padding=pool_3_padding,
data_format=channel_order)(conv_3_2)
flatten_1_2 = keras.layers.Flatten()(pool_3_2)
dense_1_f_2 = keras.layers.Dense(units=dense_1_f_units,
activation=activation_fn,
use_bias=dense_1_f_bias,
kernel_initializer=kern_init,
activity_regularizer=regularizer,
name='dense_1_f_2')(flatten_1_2)
dense_2_f_2 = keras.layers.Dense(units=dense_2_f_units,
activation=activation_fn,
use_bias=dense_2_f_bias,
kernel_initializer=kern_init,
activity_regularizer=regularizer,
name='dense_2_f_2')(dense_1_f_2)
dense_3_f_2 = keras.layers.Dense(units=dense_3_f_units,
activation=activation_fn,
use_bias=dense_3_f_bias,
kernel_initializer=kern_init,
activity_regularizer=regularizer,
name='dense_3_f_2')(dense_2_f_2)
dense_1_cali = keras.layers.Dense(units=dense_1_ca_units,
activation=activation_fn,
use_bias=dense_1_ca_bias,
kernel_initializer=kern_init,
name='dense_1_cali')(input_cali)
dense_1_comb = keras.layers.concatenate(
[dense_3_f_1, dense_3_f_2, dense_1_cali], name='dense_1_comb')
dense_2_comb = keras.layers.Dense(units=dense_2_co_units,
activation=activation_fn,
use_bias=dense_2_co_bias,
kernel_initializer=kern_init,
name='dense_2_comb')(dense_1_comb)
drop_1_comb = keras.layers.Dropout(rate=drop_1_comb_rate)(dense_2_comb)
dense_3_comb = keras.layers.Dense(units=dense_3_co_units,
activation=activation_fn,
use_bias=dense_3_co_bias,
kernel_initializer=kern_init,
name='dense_3_comb')(drop_1_comb)
main_output = keras.layers.Dense(units=main_output_units,
activation=main_output_act,
name='main_output')(dense_3_comb)
model = keras.Model(inputs=[input_fluoro_1, input_fluoro_2, input_cali],
outputs=main_output)
keras.utils.plot_model(model,
os.path.abspath(
os.path.join(save_dir, expr_name + '_' +
expr_no + '.png')),
show_shapes=True)
model.compile(optimizer=model_opt, loss=model_loss, metrics=[model_metric])
result = model.fit(x=[
np.expand_dims(X_talos[0][:, 0, :, :], axis=3),
np.expand_dims(X_talos[0][:, 1, :, :], axis=3), X_talos[1]
],
y=y_talos,
epochs=model_epochs,
batch_size=model_batchsize,
validation_data=([
np.expand_dims(X_val[0][:, 0, :, :], axis=3),
np.expand_dims(X_val[0][:, 1, :, :], axis=3),
X_val[1]
], y_val),
shuffle=True,
verbose=1)
return result, model
def test_output_dtype(self, dtype):
inputs = keras.Input(batch_size=16, shape=(4, ), dtype="float32")
layer = discretization.Discretization(bin_boundaries=[-0.5, 0.5, 1.5],
dtype=dtype)
outputs = layer(inputs)
self.assertAllEqual(outputs.dtype, dtype)
print(modelt.predict(X_train))
print(modelt.evaluate(X_test, y_test, verbose=2))
#%%
print(modelt.predict(X_test))
#%%
class TemporalSoftmax(keras.layers.Layer):
def call(self, inputs, mask=None):
broadcast_float_mask = tf.expand_dims(tf.cast(mask, "float32"), -1)
inputs_exp = tf.exp(inputs) * broadcast_float_mask
inputs_sum = tf.reduce_sum(inputs * broadcast_float_mask,
axis=1,
keepdims=True)
return inputs_exp / inputs_sum
inputs1 = keras.Input(shape=(3852))
x1 = keras.layers.Embedding(input_dim=3852, output_dim=1283,
mask_zero=True)(inputs1)
x1 = keras.layers.Dense(1)(x1)
#lstm = tf.keras.layers.LSTM(3)
outs = TemporalSoftmax()(x1)
#outputs1 = keras.layers.LSTM(1283)(x1)
modelj = keras.Model(inputs1, outputs1)
#modelj.compile(optimizer='Adam', loss='mse' ,metrics=['mae','mse','accuracy'])
#%%
modelj.fit(X_train, y_train)
}
class Sampling(layers.Layer):
"""Uses (z_mean, z_log_var) to sample z, the vector encoding a digit."""
def call(self, inputs):
z_mean, z_log_var = inputs
batch = tf.shape(z_mean)[0]
dim = tf.shape(z_mean)[1]
epsilon = tf.keras.backend.random_normal(shape=(batch, dim))
return z_mean + tf.exp(0.5 * z_log_var) * epsilon
latent_dim = 2
encoder_inputs = keras.Input(shape=(32, 32, 3))
x = layers.Conv2D(32, 3, activation="relu", strides=2,
padding="same")(encoder_inputs)
x = layers.Conv2D(64, 3, activation="relu", strides=2, padding="same")(x)
x = layers.Flatten()(x)
x = layers.Dense(16, activation="relu")(x)
z_mean = layers.Dense(latent_dim, name="z_mean")(x)
z_log_var = layers.Dense(latent_dim, name="z_log_var")(x)
z = Sampling()([z_mean, z_log_var])
encoder = keras.Model(encoder_inputs, [z_mean, z_log_var, z], name="encoder")
encoder.summary()
latent_inputs = keras.Input(shape=(latent_dim, ))
x = layers.Dense(8 * 8 * 64, activation="relu")(latent_inputs)
x = layers.Reshape((8, 8, 64))(x)
x = layers.Conv2DTranspose(64, 3, activation="relu", strides=2,
discriminator.load_weights(discriminator_save_path)
if os.path.isfile(generator_save_path):
generator.load_weights(generator_save_path)
# Use gradient clipping (by specifying a value) in the optimizer
# Use learning rate attenuation for stable training
discriminator_optimizer = keras.optimizers.Adam(lr=discriminator_lr,
beta_1=beta_1)
discriminator.compile(optimizer=discriminator_optimizer,
loss='binary_crossentropy')
# Set discriminator's weight not trained (applies to gan models only)
discriminator.trainable = False
gan_input = keras.Input(shape=(latent_dim, ))
gan_output = discriminator(generator(gan_input))
gan = keras.models.Model(gan_input, gan_output)
gan_optimizer = keras.optimizers.Adam(lr=gan_lr, beta_1=beta_1)
gan.compile(optimizer=gan_optimizer, loss='binary_crossentropy')
if not os.path.exists(save_dir):
os.mkdir(save_dir)
# Start training repetition
epoch = 0
for index, batch in enumerate(batches):
batch_size = batch.shape[0]
return norm
note = sys.argv[1]
latent_dim = 100
height = 32
width = 32
channels = 3
# Define possible opitmizer
adam_optimizer = keras.optimizers.Adam(0.0002, 0.5)
discriminator_optimizer = keras.optimizers.RMSprop(lr=0.0006, clipvalue=1.0, decay=1e-8)
gan_optimizer = keras.optimizers.RMSprop(lr=0.0006, clipvalue=1.0, decay=1e-8)
######################### MODEL BEGIN #####################################
generator_input = keras.Input(shape=(latent_dim,))
# First, transform the input into a 8x8 128-channels feature map
x = layers.Dense(128 * 8 * 8, name='g_top_layer')(generator_input)
x = layers.BatchNormalization(momentum=0.9)(x)
x = layers.Activation("relu")(x)
x = layers.Reshape((8, 8, 128))(x)
# Upsampling
x = layers.UpSampling2D()(x)
x = layers.Conv2D(128, 3, padding='same', use_bias=True)(x)
x = layers.BatchNormalization(momentum=0.9)(x)
x = layers.Activation("relu")(x)
x = layers.UpSampling2D()(x)
x = layers.Conv2D(64, 3, padding='same', use_bias=True)(x)
x = layers.BatchNormalization(momentum=0.9)(x)
def test_output_dtype(self, dtype):
inputs = keras.Input(shape=(1, ), dtype=tf.int32)
layer = category_encoding.CategoryEncoding(
num_tokens=4, output_mode=category_encoding.ONE_HOT, dtype=dtype)
outputs = layer(inputs)
self.assertAllEqual(outputs.dtype, dtype)
