def MNIST_CNY19(classes, input_shape, weights=None):
model = Sequential()
model.add(
Convolution2D(40, (5, 5),
strides=(1, 1),
input_shape=input_shape,
activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(20, (5, 5), strides=(1, 1), activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(320, activation='relu'))
model.add(Dense(160, activation='relu'))
model.add(Dense(80, activation='relu'))
model.add(Dense(40, activation='relu'))
model.add(Dense(classes, activation='softmax'))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
def __init__(self, latent_dim=49):
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
# ENCODER
inp = Input((896, 896, 1))
e = Conv2D(32, (10, 10), activation='relu')(inp)
e = MaxPooling2D((10, 10))(e)
e = Conv2D(64, (6, 6), activation='relu')(e)
e = MaxPooling2D((10, 10))(e)
e = Conv2D(64, (3, 3), activation='relu')(e)
l = Flatten()(e)
l = Dense(49, activation='softmax')(l)
# DECODER
d = Reshape((7, 7, 1))(l)
d = Conv2DTranspose(64, (3, 3),
strides=8,
activation='relu',
padding='same')(d)
d = BatchNormalization()(d)
d = Conv2DTranspose(64, (3, 3),
strides=8,
activation='relu',
padding='same')(d)
d = BatchNormalization()(d)
d = Conv2DTranspose(64, (3, 3),
strides=2,
activation='relu',
padding='same')(d)
d = BatchNormalization()(d)
d = Conv2DTranspose(32, (3, 3), activation='relu', padding='same')(d)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(d)
self.CAD = tf.keras.Model(inp, decoded)
opt = tf.keras.optimizers.RMSprop(lr=0.0001, decay=1e-6)
self.CAD.compile(loss="binary_crossentropy",
optimizer=opt,
metrics=["accuracy"])
self.Flow = tf.keras.Sequential([
tf.keras.layers.LSTM(32, input_shape=(3, 2),
return_sequences=True),
tf.keras.layers.Dropout(0.4),
tf.keras.layers.Bidirectional(
tf.keras.layers.LSTM(32, return_sequences=True)),
tf.keras.layers.Dropout(0.4),
tf.keras.layers.TimeDistributed(
tf.keras.layers.Dense(10, activation='relu')),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(2, activation='relu')
])
opt = tf.keras.optimizers.RMSprop(lr=0.0001, decay=1e-6)
self.Flow.compile(loss="binary_crossentropy",
optimizer="adam",
metrics=["accuracy"])
print(self.Flow.summary())
print(self.CAD.summary())
def CIFAR_CNY19(classes, input_shape, weights=None):
model = Sequential()
model.add(
Convolution2D(40, (5, 5), strides=(1, 1), input_shape=input_shape))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Dropout(0.25))
model.add(Convolution2D(20, (5, 5), strides=(1, 1)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(240, activation='relu'))
# model.add(Dropout(0.5))
model.add(Dense(84, activation='relu'))
# model.add(Dropout(0.5))
model.add(Dense(classes, activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
return model
def build_mnist_model(layer_data: List[int],
num_classes: int,
input_shape: Any,
learning_rate: float,
regularized: bool = False) -> Model:
model: Model = Sequential()
model.add(Flatten(input_shape=input_shape))
if regularized:
for nodes in layer_data:
model.add(
Dense(nodes,
activation="relu",
kernel_regularizer=keras.regularizers.l1(0.001)))
model.add(
Dense(num_classes,
activation="softmax",
kernel_regularizer=keras.regularizers.l1(0.001)))
else:
for nodes in layer_data:
model.add(Dense(nodes, activation="relu"))
model.add(Dense(num_classes, activation="softmax"))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adam(learning_rate),
metrics=["accuracy"])
return model
def __init__(self):
super(Z2Model, self).__init__()
# self.gcnn1 = tf.keras.layers.Conv2D(filters=20, kernel_size=(3, 3), activation='relu')
# self.gcnn2 = tf.keras.layers.Conv2D(filters=20, kernel_size=(3, 3), activation='relu')
# self.gcnn3 = tf.keras.layers.Conv2D(filters=20, kernel_size=(3, 3), activation='relu')
# self.gcnn4 = tf.keras.layers.Conv2D(filters=20, kernel_size=(3, 3), activation='relu')
# self.gcnn5 = tf.keras.layers.Conv2D(filters=20, kernel_size=(3, 3), activation='relu')
# self.gcnn6 = tf.keras.layers.Conv2D(filters=20, kernel_size=(3, 3), activation='relu')
# self.gcnn7 = tf.keras.layers.Conv2D(filters=20, kernel_size=(4, 4), activation='relu')
self.gcnn1 = ConvBatchLayer(
conv=Conv2D(filters=20, kernel_size=(3, 3), activation='relu'))
self.gcnn2 = ConvBatchLayer(
conv=Conv2D(filters=20, kernel_size=(3, 3), activation='relu'))
self.gcnn3 = ConvBatchLayer(
conv=Conv2D(filters=20, kernel_size=(3, 3), activation='relu'))
self.gcnn4 = ConvBatchLayer(
conv=Conv2D(filters=20, kernel_size=(3, 3), activation='relu'))
self.gcnn5 = ConvBatchLayer(
conv=Conv2D(filters=20, kernel_size=(3, 3), activation='relu'))
self.gcnn6 = ConvBatchLayer(
conv=Conv2D(filters=20, kernel_size=(3, 3), activation='relu'))
self.gcnn7 = ConvBatchLayer(conv=Conv2D(filters=9, kernel_size=(3, 3)))
self.flatten = Flatten()
self.dense = Dense(9)
def build_policy_network(shape, action_size, regularizer):
policy_input = Input(shape)
c1 = Conv2D(filters=1, kernel_size=1, padding='same', activation='linear',
kernel_regularizer=regularizer)(policy_input)
b1 = BatchNormalization(axis=-1)(c1)
l1 = LeakyReLU()(b1)
f1 = Flatten()(l1)
d1 = Dense(action_size, use_bias=False, activation='sigmoid', kernel_regularizer=regularizer)(f1)
policy_model = Model(inputs=policy_input, outputs=d1)
return policy_model
def build_value_network(shape, value_support_size):
value_input = Input(shape)
c1 = Conv2D(filters=1, kernel_size=1, padding='same', activation='linear')(value_input)
b1 = BatchNormalization(axis=-1)(c1)
l1 = LeakyReLU()(b1)
f1 = Flatten()(l1)
d2 = Dense(20, use_bias=False, activation='linear')(f1)
l2 = LeakyReLU()(d2)
d2 = Dense(value_support_size, use_bias=False, activation='tanh')(l2)
value_model = Model(inputs=value_input, outputs=d2)
return value_model
def build_model():
model = keras.Sequential()
model.add(
Conv2D(64, kernel_size=3, activation='relu', input_shape=(28, 28, 1)))
model.add(Conv2D(32, kernel_size=3, activation='relu'))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
return model
def build_reward_network(shape, filter_size1=3, filter_size2=1):
input = Input(shape)
c1 = Conv2D(filters=filter_size1, kernel_size=(3, 3), strides=2, padding='same', activation='relu',
input_shape=shape)(input)
a1 = AveragePooling2D(strides=2)(c1)
c2 = Conv2D(filters=filter_size2, kernel_size=(3, 3), strides=1, padding='same', activation='relu',
input_shape=shape)(a1)
a2 = AveragePooling2D(strides=2)(c2)
f1 = Flatten()(a2)
model = Model(inputs=input, outputs=f1)
return model
def SingleOutputCNN(
input_shape,
output_shape,
cnns_per_maxpool=1,
maxpool_layers=1,
dense_layers=1,
dense_units=64,
dropout=0.25,
regularization=False,
global_maxpool=False,
name='',
) -> Model:
function_name = cast(types.FrameType,
inspect.currentframe()).f_code.co_name
model_name = f"{function_name}-{name}" if name else function_name
# model_name = seq([ function_name, name ]).filter(lambda x: x).make_string("-") # remove dependency on pyfunctional - not in Kaggle repo without internet
inputs = Input(shape=input_shape)
x = inputs
for cnn1 in range(0, maxpool_layers):
for cnn2 in range(1, cnns_per_maxpool + 1):
x = Conv2D(32 * cnn2,
kernel_size=(3, 3),
padding='same',
activation='relu')(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = BatchNormalization()(x)
x = Dropout(dropout)(x)
if global_maxpool:
x = GlobalMaxPooling2D()(x)
x = Flatten()(x)
for nn1 in range(0, dense_layers):
if regularization:
x = Dense(dense_units,
activation='relu',
kernel_regularizer=regularizers.l2(0.01),
activity_regularizer=regularizers.l1(0.01))(x)
else:
x = Dense(dense_units, activation='relu')(x)
x = BatchNormalization()(x)
x = Dropout(dropout)(x)
x = Dense(output_shape, activation='softmax')(x)
model = Model(inputs, x, name=model_name)
# plot_model(model, to_file=os.path.join(os.path.dirname(__file__), f"{name}.png"))
return model
def construct_keras_model(model_type, embedding_weights):
keras_model = keras.Sequential()
keras_model.add(
Embedding(creative_id_window,
embedding_size,
input_length=max_len,
weights=[embedding_weights],
trainable=False))
if model_type == 'MLP':
keras_model.add(Flatten())
elif model_type == 'GM':
keras_model.add(GlobalMaxPooling1D())
elif model_type == 'GA':
keras_model.add(GlobalAveragePooling1D())
elif model_type == 'Conv1D':
keras_model.add(Conv1D(64, 2))
keras_model.add(MaxPooling1D())
keras_model.add(Conv1D(64, 2))
keras_model.add(MaxPooling1D())
keras_model.add(Flatten())
else:
raise Exception("错误的网络模型类型")
# keras_model.add(Dropout(0.5))
# keras_model.add(BatchNormalization())
# keras_model.add(Dense(64, activation='relu', kernel_regularizer=l2(0.001)))
keras_model.add(Dense(32, activation='relu', kernel_regularizer=l2(0.001)))
# keras_model.add(Dropout(0.5))
# keras_model.add(BatchNormalization())
keras_model.add(
Dense(1, activation='sigmoid', kernel_regularizer=l2(0.001)))
keras_model.summary()
# print("保存模型的原始结构：", keras_model.save('model/word2vec/{0}_m0_{1}.h5'.format(model_type, label_name)))
keras_model.compile(optimizer=optimizers.RMSprop(lr=RMSProp_lr),
loss=losses.binary_crossentropy,
metrics=[metrics.binary_accuracy])
return keras_model
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 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 _build_model(self, hl1_dims, hl2_dims, hl3_dims, input_layer_size,
output_layer_size, optimizer, loss):
input_v = Input(shape=(1, input_layer_size))
branch_v = Flatten()(input_v)
branch_v = Dense(hl1_dims, activation='relu')(branch_v)
branch_v = BatchNormalization()(branch_v)
branch_v = Dense(hl2_dims, activation='relu')(branch_v)
branch_v = BatchNormalization()(branch_v)
out_v = Dense(hl3_dims, activation='relu')(branch_v)
out_v = BatchNormalization()(out_v)
out_v = Dense(output_layer_size, activation='linear')(out_v)
model = Model(inputs=input_v, outputs=out_v)
#model = Model(inputs=m_v.inputs, outputs=out_v)
# print(model.summary())
model.compile(
optimizer=optimizer, loss=loss
) # Use Huber Loss Function for DQN based on TensorBoard analysis
return model
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'])
plt.imshow(ims[i], interpolation=None if interp else 'none')
# one-hot encoding [x,y] = [cat,dog] ==> of [1, 0], and cats are represented by [0,1]
#plots(imgs, titles=labels)
#plt.show()
model = Sequential([
# 2-dimensional convolutional layer because of 2D image
# You can experiment by choosing different values for these parameters.
# 32 output filters
# Kernel of 3*3
# input_shape is resizing the image so it will take less time. FOr example to 64*64
Conv2D(filters=32, kernel_size=(3, 3), activation='relu', input_shape=(224,224,3)),
# We’ll then Flatten the output from the convolutional layer and pass it to a Dense layer
# Take previous layer to 1d tensor
Flatten(),
# Output for 2 options: cats or dogs
Dense(2, activation='softmax'),
])
model.summary()
model.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])
model.fit_generator(generator=train_batches, steps_per_epoch=4,
validation_data=valid_batches, validation_steps=4, epochs=5, verbose=2)
test_imgs, test_labels = next(test_batches)
# show images with encoding
#plots(test_imgs, titles=test_labels)
#plt.show()
# dataset reshape
# array -> category
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
# scaling
X_train = X_train / 255.0
X_test = X_test / 255.0
# modeling
model = Sequential()
model.add(Dense(784, input_shape=(
28,
28,
), activation='relu'))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.summary()
#compiling models
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# input dataset in model
model.fit(X_train, y_train, batch_size=200, epochs=1, validation_split=0.2)
'''
48000/48000 [==============================] - 22s 462us/sample - loss: 0.2445 - accuracy: 0.9248 - val_loss: 0.1263 - val_accuracy: 0.9633
'''
def build_model(self, model_name, query_dim, terms_dim, output_dim,
word_embedding):
self.model_name = model_name
query_input = Input(shape=(query_dim, ), name='query_input')
terms_input = Input(shape=(terms_dim, ), name='terms_input')
if model_name == 'lstm':
embedding_feature_block = Sequential(layers=[
Embedding(word_embedding.vocabulary_size,
word_embedding.dimensions,
weights=[word_embedding.embedding_matrix],
trainable=True,
mask_zero=False),
BatchNormalization(),
LSTM(64, return_sequences=True)
])
elif model_name == 'bilstm':
embedding_feature_block = Sequential(layers=[
Embedding(word_embedding.vocabulary_size,
word_embedding.dimensions,
weights=[word_embedding.embedding_matrix],
trainable=True,
mask_zero=False),
BatchNormalization(),
Bidirectional(LSTM(64, return_sequences=True))
])
else: # default cnn
embedding_feature_block = Sequential(layers=[
Embedding(word_embedding.vocabulary_size,
word_embedding.dimensions,
weights=[word_embedding.embedding_matrix],
trainable=True,
mask_zero=False),
BatchNormalization(),
Conv1D(filters=64, kernel_size=3, strides=1),
MaxPooling1D(pool_size=3)
])
# Features
query_feature = embedding_feature_block(query_input)
terms_feature = embedding_feature_block(terms_input)
# Query-Terms alignment
attention = Dot(axes=-1)([query_feature, terms_feature])
softmax_attention = Lambda(lambda x: softmax(x, axis=1),
output_shape=unchanged_shape)(attention)
terms_aligned = Dot(axes=1)([softmax_attention, terms_feature])
# Aligned features
if model_name == 'lstm':
flatten_layer = LSTM(128, return_sequences=False)(terms_aligned)
elif model_name == 'bilstm':
flatten_layer = Bidirectional(LSTM(
128, return_sequences=False))(terms_aligned)
else: # default cnn
merged_cnn = Conv1D(filters=128, kernel_size=3,
strides=1)(terms_aligned)
merged_cnn = MaxPooling1D(pool_size=3)(merged_cnn)
flatten_layer = Flatten()(merged_cnn)
# Output
dense = BatchNormalization()(flatten_layer)
dense = Dense(64, activation='sigmoid')(dense)
out = Dense(output_dim, activation='linear')(dense)
self.model = Model(inputs=[query_input, terms_input], outputs=out)
self.model.compile(optimizer='adam', loss=losses.mean_squared_error)
self.model.summary()
