#1. evrişim katmanı model.add(Conv2D(64, (5, 5), activation='relu', input_shape=(48,48,1))) model.add(MaxPooling2D(pool_size=(5,5), strides=(2, 2))) #2. Evrişim katmanı model.add(Conv2D(64, (3, 3), activation='relu')) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(AveragePooling2D(pool_size=(3,3), strides=(2, 2))) #3. Evrişim katmanı model.add(Conv2D(128, (3, 3), activation='relu')) model.add(Conv2D(128, (3, 3), activation='relu')) model.add(AveragePooling2D(pool_size=(3,3), strides=(2, 2))) model.add(Flatten()) # Tam bağlantı katmanı model.add(Dense(1024, activation='relu')) model.add(Dropout(0.2)) model.add(Dense(1024, activation='relu')) model.add(Dropout(0.2)) model.add(Dense(num_classes, activation='softmax')) #------------------------------ #Batch (Küme) işlemleri gen = ImageDataGenerator() train_generator = gen.flow(x_train, y_train, batch_size=batch_size) #------------------------------
def PointNet(classes=40, load_weights=True, num_points =2048):
num_points = num_points
input_points = Input(shape=(num_points, 3))
x = Convolution1D(64, 1, activation='relu',
input_shape=(num_points, 3))(input_points)
x = BatchNormalization()(x)
x = Convolution1D(128, 1, activation='relu')(x)
x = BatchNormalization()(x)
x = Convolution1D(1024, 1, activation='relu')(x)
x = BatchNormalization()(x)
x = MaxPooling1D(pool_size=num_points)(x)
x = Dense(512, activation='relu')(x)
x = BatchNormalization()(x)
x = Dense(256, activation='relu')(x)
x = BatchNormalization()(x)
x = Dense(9, weights=[np.zeros([256, 9]), np.array([1, 0, 0, 0, 1, 0, 0, 0, 1]).astype(np.float32)])(x)
input_T = Reshape((3, 3))(x)
# forward net
g = Lambda(mat_mul, arguments={'B': input_T})(input_points)
g = Convolution1D(64, 1, input_shape=(num_points, 3), activation='relu')(g)
g = BatchNormalization()(g)
g = Convolution1D(64, 1, input_shape=(num_points, 3), activation='relu')(g)
g = BatchNormalization()(g)
# feature transform net
f = Convolution1D(64, 1, activation='relu')(g)
f = BatchNormalization()(f)
f = Convolution1D(128, 1, activation='relu')(f)
f = BatchNormalization()(f)
f = Convolution1D(1024, 1, activation='relu')(f)
f = BatchNormalization()(f)
f = MaxPooling1D(pool_size=num_points)(f)
f = Dense(512, activation='relu')(f)
f = BatchNormalization()(f)
f = Dense(256, activation='relu')(f)
f = BatchNormalization()(f)
f = Dense(64 * 64, weights=[np.zeros([256, 64 * 64]), np.eye(64).flatten().astype(np.float32)])(f)
feature_T = Reshape((64, 64))(f)
# forward net
g = Lambda(mat_mul, arguments={'B': feature_T})(g)
g = Convolution1D(64, 1, activation='relu')(g)
g = BatchNormalization()(g)
g = Convolution1D(128, 1, activation='relu')(g)
g = BatchNormalization()(g)
g = Convolution1D(1024, 1, activation='relu')(g)
g = BatchNormalization()(g)
# global_feature
global_feature = MaxPooling1D(pool_size=num_points)(g)
# point_net_cls
c = Dense(512, activation='relu')(global_feature)
c = BatchNormalization()(c)
c = Dropout(rate=0.7)(c)
c = Dense(256, activation='relu')(c)
c = BatchNormalization()(c)
c = Dropout(rate=0.7)(c)
c = Dense(classes, activation='softmax')(c)
prediction = Flatten()(c)
model = Model(inputs=input_points, outputs=prediction)
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
if(classes == 40 and load_weights):
model.load_weights('Models/PointNet-ModelNet40.h5')
if(classes == 10 and load_weights):
model.load_weights('Models/PointNet-ModelNet10.h5')
if(classes == 2 and load_weights):
model.load_weights('Models/PointNet-KITTI.h5')
if(classes == 3 and load_weights):
model.load_weights('Models/PointNet-KITTI3.h5')
print model.summary()
return model
train_data_dir = 'data/train'
validation_data_dir = 'data/validation'
nb_train_samples = 2000
nb_validation_samples = 800
epochs = 50
batch_size = 16
# build the VGG16 network
base_model = applications.VGG16(weights='imagenet',
include_top=False,
input_shape=(150, 150, 3))
print('Model loaded.')
# build a classifier model to put on top of the convolutional model
top_model = Sequential()
top_model.add(Flatten(input_shape=base_model.output_shape[1:]))
top_model.add(Dense(256, activation='relu'))
top_model.add(Dropout(0.5))
top_model.add(Dense(1, activation='sigmoid'))
# note that it is necessary to start with a fully-trained
# classifier, including the top classifier,
# in order to successfully do fine-tuning
top_model.load_weights(top_model_weights_path)
# add the model on top of the convolutional base
# model.add(top_model)
model = Model(inputs=base_model.input, outputs=top_model(base_model.output))
# set the first 25 layers (up to the last conv block)
# to non-trainable (weights will not be updated)
def run_cnn(deep_data_processor, model_type):
x_train, x_test, y_train, y_test, vocabulary, vocabulary_inv = deep_data_processor.output
#w2v_type = "trained" # trained | pre-trained
# Model Hyperparameters
embedding_dim = 300 # 50 | 300
filter_sizes = (3, 4, 5)
num_filters = 10
dropout_prob = (0.5, 0.8)
hidden_dims = 100
# Training parameters
batch_size = 64
num_epochs = 4
# Prepossessing parameters
sequence_length = 400
# Word2Vec parameters (see train_word2vec)
min_word_count = 1
context = 10
x_train = sequence.pad_sequences(x_train,
maxlen=sequence_length,
padding="post",
truncating="post")
x_test = sequence.pad_sequences(x_test,
maxlen=sequence_length,
padding="post",
truncating="post")
# Prepare embedding layer weights and convert inputs for static model
print("Model type is", model_type)
if model_type == "non-static":
#if w2v_type == "trained":
embedding_weights = train_word2vec(np.vstack((x_train, x_test)),
vocabulary_inv,
num_features=embedding_dim,
min_word_count=min_word_count,
context=context)
# elif w2v_type == "pre-trained":
# embedding_weights = load_google_trained_w2v(vocabulary_inv, embedding_dim)
elif model_type == "rand":
embedding_weights = None
else:
raise ValueError("Unknown model type")
# Build model
input_shape = (sequence_length, )
model_input = Input(shape=input_shape)
# Static model do not have embedding layer
z = Embedding(len(vocabulary_inv),
embedding_dim,
input_length=sequence_length,
name="embedding")(model_input)
z = Dropout(dropout_prob[0])(z)
# Convolutional block
conv_blocks = []
for sz in filter_sizes:
conv = Convolution1D(filters=num_filters,
kernel_size=sz,
padding="valid",
activation="relu",
strides=1)(z)
conv = MaxPooling1D(pool_size=2)(conv)
conv = Flatten()(conv)
conv_blocks.append(conv)
z = Concatenate()(conv_blocks) if len(conv_blocks) > 1 else conv_blocks[0]
z = Dropout(dropout_prob[1])(z)
z = Dense(hidden_dims, activation="relu")(z)
model_output = Dense(1, activation="sigmoid")(z)
model = Model(model_input, model_output)
model.compile(loss="binary_crossentropy",
optimizer="adam",
metrics=["accuracy"])
# Initialize weights with word2vec
if model_type == "non-static":
embedding_layer = model.get_layer("embedding")
embedding_layer.set_weights(embedding_weights)
# Train the model
model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=num_epochs,
validation_data=(x_test, y_test))
scores = model.evaluate(x_test, y_test, verbose=0)
print("Accuracy: %.2f%%" % (scores[1] * 100))
def train_on_VeRi(model, out_name, fix_layer =-1, xml_file = 'train_label.xml', image_folder = 'image_train',ismodelfile = True):
nb_epoch = 70
num_classes = 776
batch_size = 32
labels = []
train_names = []
xmlp = ET.XMLParser(encoding="utf-8")
# train label file - xml
f = ET.parse(xml_file, parser=xmlp)
root = f.getroot()
for child in root.iter('Item'):
labels.append(child.attrib['vehicleID'])
train_names.append(os.path.join(image_folder, child.attrib['imageName']))
labels = utils.to_categorical(labels, num_classes=776)
X_train = []
for name in train_names:
x = load_img(name, target_size=(img_rows, img_cols))
x = img_to_array(x)
X_train.append(x)
X_train = np.array(X_train, ndmin=4)
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
train_generator = train_datagen.flow(
x=X_train, y=labels,
batch_size=batch_size)
if ismodelfile:
base_model = load_model(model)
else:
base_model = model
x = base_model.get_layer(name='block5_pool').output
x = Flatten()(x)
x = Dense(4096, activation='relu', name='fc1')(x)
x = Dense(4096, activation='relu', name='fc2')(x)
x = Dropout(0.5)(x)
predictions = Dense(num_classes, activation='softmax')(x)
model = Model(input=base_model.input, output=predictions)
if fix_layer > -1:
for layer in model.layers[:fix_layer]:
layer.trainable = False
sgd = SGD(lr=1e-3)
model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])
model.fit_generator(
train_generator,
epochs=nb_epoch,
steps_per_epoch=1184,
)
model.save(out_name)
return model
# Step 2 - Perform Max Pooling and add layers # Pooling reduces the size of the feature map # by applying matrix which picks only maximum element # in the original feature map classifier.add(MaxPooling2D(pool_size = (2, 2))) # Adding a second convolutional layer and pooled layer classifier.add(Conv2D(32, (3, 3), activation = 'relu')) classifier.add(MaxPooling2D(pool_size = (2, 2))) # Step 3 - Flattening - converts pooled and convolved features into one bit vector classifier.add(Flatten()) # Step 4 - Full connection - responsible for full connection through layers # Fully connected layer is hidden layer same as in ANN # Esentially this part is to construct ANN classifier.add(Dense(units = 128, activation = 'relu')) # Output layer classifier.add(Dense(units = 1, activation = 'sigmoid')) # Adding output layer completes the full connections
def resnet_v2(input_shape, depth, num_classes=10):
"""ResNet Version 2 Model builder [b]
Stacks of (1 x 1)-(3 x 3)-(1 x 1) BN-ReLU-Conv2D or also known as
bottleneck layer
First shortcut connection per layer is 1 x 1 Conv2D.
Second and onwards shortcut connection is identity.
At the beginning of each stage, the feature map size is halved (downsampled)
by a convolutional layer with strides=2, while the number of filter maps is
doubled. Within each stage, the layers have the same number filters and the
same filter map sizes.
Features maps sizes:
conv1 : 32x32, 16
stage 0: 32x32, 64
stage 1: 16x16, 128
stage 2: 8x8, 256
# Arguments
input_shape (tensor): shape of input image tensor
depth (int): number of core convolutional layers
num_classes (int): number of classes (CIFAR10 has 10)
# Returns
model (Model): Keras model instance
"""
if (depth - 2) % 9 != 0:
raise ValueError('depth should be 9n+2 (eg 56 or 110 in [b])')
# Start model definition.
num_filters_in = 16
num_res_blocks = int((depth - 2) / 9)
inputs = Input(shape=input_shape)
# v2 performs Conv2D with BN-ReLU on input before splitting into 2 paths
x = resnet_layer(inputs=inputs,
num_filters=num_filters_in,
conv_first=True)
# Instantiate the stack of residual units
for stage in range(3):
for res_block in range(num_res_blocks):
activation = 'relu'
batch_normalization = True
strides = 1
if stage == 0:
num_filters_out = num_filters_in * 4
if res_block == 0: # first layer and first stage
activation = None
batch_normalization = False
else:
num_filters_out = num_filters_in * 2
if res_block == 0: # first layer but not first stage
strides = 2 # downsample
# bottleneck residual unit
y = resnet_layer(inputs=x,
num_filters=num_filters_in,
kernel_size=1,
strides=strides,
activation=activation,
batch_normalization=batch_normalization,
conv_first=False)
y = resnet_layer(inputs=y,
num_filters=num_filters_in,
conv_first=False)
y = resnet_layer(inputs=y,
num_filters=num_filters_out,
kernel_size=1,
conv_first=False)
if res_block == 0:
# linear projection residual shortcut connection to match
# changed dims
x = resnet_layer(inputs=x,
num_filters=num_filters_out,
kernel_size=1,
strides=strides,
activation=None,
batch_normalization=False)
x = keras.layers.add([x, y])
num_filters_in = num_filters_out
# Add classifier on top.
# v2 has BN-ReLU before Pooling
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = AveragePooling2D(pool_size=8)(x)
y = Flatten()(x)
outputs = Dense(num_classes,
activation='softmax',
kernel_initializer='he_normal')(y)
# Instantiate model.
model = Model(inputs=inputs, outputs=outputs)
return model
from keras.utils import np_utils
# load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
img_width = X_train.shape[1]
img_height = X_train.shape[2]
X_train = X_train.astype('float32')
X_train /= 255.
X_test = X_test.astype('float32')
X_test /= 255.
# one hot encode outputs
y_train = np_utils.to_categorical(y_train)
num_classes = y_train.shape[1]
y_test = np_utils.to_categorical(y_test)
# create model
model = Sequential()
model.add(Flatten(input_shape=(img_width, img_height)))
model.add(Dropout(0.5))
model.add(Dense(30, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# Fit the model
model.fit(X_train, y_train, validation_data=(X_test, y_test))
Conv2d4= Conv2D(filters= 21, kernel_size=3, padding="same", activation="relu")(Conv2d3) drop2 = Dropout(0.3)(Conv2d4) Conv2d5= Conv2D(filters= 21, kernel_size=3, padding="same", activation="relu")(drop2) drop3 = Dropout(0.3)(Conv2d5) Conv2d6= Conv2D(filters= 12, kernel_size=3, padding="same", activation="relu")(drop3) drop4 = Dropout(0.3)(Conv2d6) Conv2d7= Conv2D(filters= 18, kernel_size=3, padding="same", activation="relu")(drop4) drop5 = Dropout(0.1)(Conv2d7) Conv2d8= Conv2D(filters= 12, kernel_size=3, padding="same", activation="relu")(drop5) drop6 = Dropout(0.1)(Conv2d8) output1 = (Flatten())(drop6) output2 = Dense(10, activation = 'softmax')(output1) model = Model(inputs=input1, outputs=output2) model.summary() print(x_train) print(y_train) #3. 훈련 model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy']) model.fit(x_train, y_train, epochs = 50) #acc75프로로 잡아라 #4. 예측 loss,acc = model.evaluate(x_test,y_test, batch_size=30)
def gen_model():
m_in = Input(shape=x[0][0].shape)
m_off = Lambda(offset_slice)(m_in)
m_noise = GaussianNoise(np.std(x[0][0] / 100))(m_off) # how much noice to have????
m_t = Conv1D(22, 20, padding='causal')(m_noise)
m_t = BatchNormalization()(m_t)
m_t = ELU()(m_t)
m_t = AveragePooling1D(2)(m_t)
m_t = Dropout(0.3)(m_t)
m_t = Conv1D(15, 20, padding='causal')(m_t)
m_t = BatchNormalization()(m_t)
m_t = ELU()(m_t)
m_t = AveragePooling1D(2)(m_t)
m_t = Dropout(0.5)(m_t)
m_t = Conv1D(10, 12, padding='causal')(m_t)
m_t = BatchNormalization()(m_t)
m_t = ELU()(m_t)
m_t = AveragePooling1D(2)(m_t)
m_t = Dropout(0.6)(m_t)
m_t = Flatten()(m_t)
m_t = Dense(35)(m_t)
m_t = BatchNormalization()(m_t)
m_t = Activation('tanh')(m_t)
m_t = Dense(15)(m_t)
m_t = BatchNormalization()(m_t)
m_t = Activation('tanh')(m_t)
m_out1 = Dense(1)(m_t)
m_t = Conv1D(22, 20, padding='causal')(m_noise)
m_t = BatchNormalization()(m_t)
m_t = ELU()(m_t)
m_t = AveragePooling1D(2)(m_t)
m_t = Dropout(0.3)(m_t)
m_t = Conv1D(15, 20, padding='causal')(m_t)
m_t = BatchNormalization()(m_t)
m_t = ELU()(m_t)
m_t = AveragePooling1D(2)(m_t)
m_t = Dropout(0.5)(m_t)
m_t = Conv1D(10, 12, padding='causal')(m_t)
m_t = BatchNormalization()(m_t)
m_t = ELU()(m_t)
m_t = AveragePooling1D(2)(m_t)
m_t = Dropout(0.6)(m_t)
m_t = Flatten()(m_t)
m_t = Dense(35)(m_t)
m_t = BatchNormalization()(m_t)
m_t = Activation('tanh')(m_t)
m_t = Dense(15)(m_t)
m_t = BatchNormalization()(m_t)
m_t = Activation('tanh')(m_t)
m_out2 = Dense(1)(m_t)
m_t = Conv1D(22, 20, padding='causal')(m_noise)
m_t = BatchNormalization()(m_t)
m_t = ELU()(m_t)
m_t = AveragePooling1D(2)(m_t)
m_t = Dropout(0.3)(m_t)
m_t = Conv1D(15, 20, padding='causal')(m_t)
m_t = BatchNormalization()(m_t)
m_t = ELU()(m_t)
m_t = AveragePooling1D(2)(m_t)
m_t = Dropout(0.5)(m_t)
m_t = Conv1D(10, 12, padding='causal')(m_t)
m_t = BatchNormalization()(m_t)
m_t = ELU()(m_t)
m_t = AveragePooling1D(2)(m_t)
m_t = Dropout(0.6)(m_t)
m_t = Flatten()(m_t)
m_t = Dense(35)(m_t)
m_t = BatchNormalization()(m_t)
m_t = Activation('tanh')(m_t)
m_t = Dense(15)(m_t)
m_t = BatchNormalization()(m_t)
m_t = Activation('tanh')(m_t)
m_out3 = Dense(1)(m_t)
m_conc = concatenate([m_out1, m_out2, m_out3])
m_out = Activation('softmax')(m_conc)
model = Model(inputs=m_in, outputs=m_out)
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
return model
train_path = 'C:/Users/hp/Documents/Machine Learning Projects/Pneumonia classification/chest_xray/train'
test_path = 'C:/Users/hp/Documents/Machine Learning Projects/Pneumonia classification/chest_xray/test'
vgg = VGG16(input_shape=Image_Size + [3],
weights='imagenet',
include_top=False)
for layer in vgg.layers:
layer.trainable = False
folders = glob(
'C:/Users/hp/Documents/Machine Learning Projects/Pneumonia classification/chest_xray/train/*'
)
x = Flatten()(vgg.output)
prediction = Dense(len(folders), activation='softmax')(x)
model = Model(inputs=vgg.input, outputs=prediction)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
train_datagen = ImageDataGenerator(rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1. / 255)
def build_model(max_length: int,
embedding_matrix: Union[np.ndarray, Tuple[int, int]],
filters: List[int],
kernel_size: List[int],
pool_size: List[int],
conv_padding: str,
pool_padding: str,
cell_type: str,
cell_size: int,
cell_stack_size: int,
dense_size: List[int],
embedding_dropout: float = 0.6,
embedding_not_trainable: bool = False,
conv_dropout: float = 0.1,
dense_dropout: Union[float, List[float]] = 0.3,
classifier_dropout: float = 0.1,
rnn_first: bool = False) -> Model:
if not (len(filters) > 0 and len(kernel_size) > 0 and len(pool_size) > 0):
logger.error(
"There are no filters, kernel sizes or pool sizes specified for the CNN."
)
raise ValueError(
"There are no filters, kernel sizes or pool sizes specified for the CNN."
)
if type(dense_dropout) != list:
dense_dropout = [dense_dropout]
if len(dense_size) > 0 and len(dense_size) != len(dense_dropout):
max_list_length = max([len(dense_size), len(dense_dropout)])
new_dense_size = []
new_dense_dropout = []
for i in range(max_list_length):
new_dense_size.append(
dense_size[i] if i < len(dense_size) else dense_size[-1])
new_dense_dropout.append(dense_dropout[i] if i < len(dense_dropout)
else dense_dropout[-1])
dense_size = new_dense_size
dense_dropout = new_dense_dropout
logger.warning(
"Lists given for dense layer sizes and dense layer dropout rates are not the same length. "
"The shorter lists are padded using the last value to match the length of the longest."
)
if len(filters) != len(kernel_size) or len(filters) != len(
pool_size) or len(kernel_size) != len(pool_size):
max_list_length = max([len(filters), len(kernel_size), len(pool_size)])
new_filters = []
new_kernel_size = []
new_pool_size = []
for i in range(max_list_length):
new_filters.append(filters[i] if i < len(filters) else filters[-1])
new_kernel_size.append(
kernel_size[i] if i < len(kernel_size) else kernel_size[-1])
new_pool_size.append(
pool_size[i] if i < len(pool_size) else pool_size[-1])
filters = new_filters
kernel_size = new_kernel_size
pool_size = new_pool_size
logger.warning(
"Lists given for convolutional filters, kernel sizes and pooling sizes had different lengths. "
"The shorter lists are padded using the last value to match the length of the longest."
)
cell_type_name = cell_type.lower()
if cell_type_name == "lstm":
cell_type = LSTM
elif cell_type_name == "gru":
cell_type = GRU
# input 1: word indices, input 2: handcrafted_features
raw_input = Input(shape=(max_length, ), name="word_input")
# embedding layer
embedding_layer = Embedding(
input_dim=(embedding_matrix[0] if type(embedding_matrix) == tuple else
embedding_matrix.shape[0]),
output_dim=(embedding_matrix[1] if type(embedding_matrix) == tuple else
embedding_matrix.shape[1]),
input_length=max_length,
name="word_embedding",
weights=(None
if type(embedding_matrix) == tuple else [embedding_matrix]),
trainable=(not embedding_not_trainable))
embedding_dropout = Dropout(embedding_dropout, name="embedding_dropout")
# convolutional layer(s)
conv_layers = []
for i in range(len(filters)):
conv_layer_name = "conv_{}".format(i)
convolution = Conv1D(filters[i],
kernel_size[i],
padding=conv_padding,
activation="relu",
name=conv_layer_name)
pooling = MaxPooling1D(pool_size[i],
padding=pool_padding,
name="max_pool_{}".format(i))
conv_layers.append((convolution, pooling))
conv_dropout = Dropout(conv_dropout, name="conv_dropout")
# bidirectional RNN
rnn_cells = []
for i in range(cell_stack_size - 1):
cell_name = "{}_{}".format(cell_type_name, i)
cell = Bidirectional(cell_type(cell_size,
return_sequences=True,
name=cell_name),
name="bidirectional_{}".format(cell_name))
rnn_cells.append(cell)
# last cell
cell_name = "{}_{}".format(cell_type_name, cell_stack_size - 1)
cell = Bidirectional(
cell_type(cell_size, return_sequences=rnn_first, name=cell_name))
rnn_cells.append(cell)
# dense layer(s)
dense_layers = []
for i in range(len(dense_size)):
dropout = Dropout(rate=dense_dropout[i],
name="dense_dropout_{}".format(i))
dense_layer_name = "dense_{}".format(i)
dense = Dense(dense_size[i], name=dense_layer_name)
dense_layers.append((dropout, dense))
# classification layer
classifier_dropout = Dropout(classifier_dropout, name="classifier_dropout")
classifier = Dense(1, name="classifier")
classifier_prediction = Activation("sigmoid", name="classifier_prediction")
# build the actual model
output = embedding_dropout(embedding_layer(raw_input))
if rnn_first:
for c in rnn_cells:
output = c(output)
for l in conv_layers:
output = l[1](l[0](conv_dropout(output)))
output = Flatten(name="flatten")(output)
else:
for l in conv_layers:
output = conv_dropout(l[1](l[0](output)))
for c in rnn_cells:
output = c(output)
for l in dense_layers:
output = l[1](l[0](output))
output = classifier_prediction(classifier(classifier_dropout(output)))
model = Model(inputs=raw_input, outputs=output)
return model
testdf['general_cat'], _, _ = \
zip(*testdf['category_name'].apply(lambda x: split_cat(x)))
indexcatesub = testdf['general_cat']
del(testdf)
if True:
input1 = Input(shape=(sub_featlen.shape[1],), dtype='float32')
input2 = Input(shape=(1,), dtype='int32')
embedding_layer0 = Embedding(index3,
30,init = RandomNormal(mean=0.0, stddev=0.005),
input_length=1,
trainable=True)
xc3 = embedding_layer0(input2)
xc3 = Flatten()(xc3)
input3 = Input(shape=(FEAT_LENGTH-FIX_LEN,), dtype='int32')
embedding_layer0 = Embedding(wordnum,
40,
init = RandomNormal(mean=0.0, stddev=0.005),
trainable=True)
x30 = embedding_layer0(input3)
x31 = Cropping1D(cropping=(0,40))(x30)
la = []
x3 = Conv1D(15,3,activation='sigmoid',padding = 'same',dilation_rate = 1)(x31)
x3 = MaxPooling1D(FEAT_LENGTH-FIX_LEN)(x3)
x3 = Flatten()(x3)
la.append(x3)
t=Conv1D(50,2,activation='sigmoid',padding = 'same',dilation_rate = 1)
x3 = t(x31)
def main():
options = parse_inputs()
c = color_codes()
# Prepare the net architecture parameters
sequential = options['sequential']
dfactor = options['dfactor']
# Prepare the net hyperparameters
num_classes = 5
epochs = options['epochs']
padding = options['padding']
patch_width = options['patch_width']
patch_size = (patch_width, patch_width, patch_width)
batch_size = options['batch_size']
dense_size = options['dense_size']
conv_blocks = options['conv_blocks']
n_filters = options['n_filters']
filters_list = n_filters if len(n_filters) > 1 else n_filters*conv_blocks
conv_width = options['conv_width']
kernel_size_list = conv_width if isinstance(conv_width, list) else [conv_width]*conv_blocks
balanced = options['balanced']
recurrent = options['recurrent']
# Data loading parameters
preload = options['preload']
queue = options['queue']
# Prepare the sufix that will be added to the results for the net and images
path = options['dir_name']
filters_s = 'n'.join(['%d' % nf for nf in filters_list])
conv_s = 'c'.join(['%d' % cs for cs in kernel_size_list])
s_s = '.s' if sequential else '.f'
ub_s = '.ub' if not balanced else ''
params_s = (ub_s, dfactor, s_s, patch_width, conv_s, filters_s, dense_size, epochs, padding)
sufix = '%s.D%d%s.p%d.c%s.n%s.d%d.e%d.pad_%s.' % params_s
n_channels = np.count_nonzero([
options['use_flair'],
options['use_t2'],
options['use_t1'],
options['use_t1ce']]
)
print(c['c'] + '[' + strftime("%H:%M:%S") + '] ' + 'Starting cross-validation' + c['nc'])
# N-fold cross validation main loop (we'll do 2 training iterations with testing for each patient)
data_names, label_names = get_names_from_path(options)
folds = options['folds']
fold_generator = izip(nfold_cross_validation(data_names, label_names, n=folds, val_data=0.25), xrange(folds))
dsc_results = list()
for (train_data, train_labels, val_data, val_labels, test_data, test_labels), i in fold_generator:
print(c['c'] + '[' + strftime("%H:%M:%S") + '] ' + c['nc'] + 'Fold %d/%d: ' % (i+1, folds) + c['g'] +
'Number of training/validation/testing images (%d=%d/%d=%d/%d)'
% (len(train_data), len(train_labels), len(val_data), len(val_labels), len(test_data)) + c['nc'])
# Prepare the data relevant to the leave-one-out (subtract the patient from the dataset and set the path)
# Also, prepare the network
net_name = os.path.join(path, 'baseline-brats2017.fold%d' % i + sufix + 'mdl')
# First we check that we did not train for that patient, in order to save time
try:
# net_name_before = os.path.join(path,'baseline-brats2017.fold0.D500.f.p13.c3c3c3c3c3.n32n32n32n32n32.d256.e1.pad_valid.mdl')
net = keras.models.load_model(net_name)
print '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++'
print 'load net successfully'
except IOError:
print '==============================================================='
# NET definition using Keras
train_centers = get_cnn_centers(train_data[:, 0], train_labels, balanced=balanced)
val_centers = get_cnn_centers(val_data[:, 0], val_labels, balanced=balanced)
train_samples = len(train_centers)/dfactor
val_samples = len(val_centers) / dfactor
print(c['c'] + '[' + strftime("%H:%M:%S") + '] ' + c['g'] + 'Creating and compiling the model ' +
c['b'] + '(%d samples)' % train_samples + c['nc'])
train_steps_per_epoch = -(-train_samples/batch_size)
val_steps_per_epoch = -(-val_samples / batch_size)
input_shape = (n_channels,) + patch_size
if sequential:
# Sequential model that merges all 4 images. This architecture is just a set of convolutional blocks
# that end in a dense layer. This is supposed to be an original baseline.
net = Sequential()
net.add(Conv3D(
filters_list[0],
kernel_size=kernel_size_list[0],
input_shape=input_shape,
activation='relu',
data_format='channels_first'
))
for filters, kernel_size in zip(filters_list[1:], kernel_size_list[1:]):
net.add(Dropout(0.5))
net.add(Conv3D(filters, kernel_size=kernel_size, activation='relu', data_format='channels_first'))
net.add(Dropout(0.5))
net.add(Flatten())
net.add(Dense(dense_size, activation='relu'))
net.add(Dropout(0.5))
net.add(Dense(num_classes, activation='softmax'))
else:
# This architecture is based on the functional Keras API to introduce 3 output paths:
# - Whole tumor segmentation
# - Core segmentation (including whole tumor)
# - Whole segmentation (tumor, core and enhancing parts)
# The idea is to let the network work on the three parts to improve the multiclass segmentation.
merged_inputs = Input(shape=(4,) + patch_size, name='merged_inputs')
flair = Reshape((1,) + patch_size)(
Lambda(
lambda l: l[:, 0, :, :, :],
output_shape=(1,) + patch_size)(merged_inputs),
)
t2 = Reshape((1,) + patch_size)(
Lambda(lambda l: l[:, 1, :, :, :], output_shape=(1,) + patch_size)(merged_inputs)
)
t1 = Lambda(lambda l: l[:, 2:, :, :, :], output_shape=(2,) + patch_size)(merged_inputs)
for filters, kernel_size in zip(filters_list, kernel_size_list):
flair = Conv3D(filters,
kernel_size=kernel_size,
activation='relu',
data_format='channels_first'
)(flair)
t2 = Conv3D(filters,
kernel_size=kernel_size,
activation='relu',
data_format='channels_first'
)(t2)
t1 = Conv3D(filters,
kernel_size=kernel_size,
activation='relu',
data_format='channels_first'
)(t1)
flair = Dropout(0.5)(flair)
t2 = Dropout(0.5)(t2)
t1 = Dropout(0.5)(t1)
# We only apply the RCNN to the multioutput approach (we keep the simple one, simple)
if recurrent:
flair = Conv3D(
dense_size,
kernel_size=(1, 1, 1),
activation='relu',
data_format='channels_first',
name='fcn_flair'
)(flair)
flair = Dropout(0.5)(flair)
t2 = concatenate([flair, t2], axis=1)
t2 = Conv3D(
dense_size,
kernel_size=(1, 1, 1),
activation='relu',
data_format='channels_first',
name='fcn_t2'
)(t2)
t2 = Dropout(0.5)(t2)
t1 = concatenate([t2, t1], axis=1)
t1 = Conv3D(
dense_size,
kernel_size=(1, 1, 1),
activation='relu',
data_format='channels_first',
name='fcn_t1'
)(t1)
t1 = Dropout(0.5)(t1)
flair = Dropout(0.5)(flair)
t2 = Dropout(0.5)(t2)
t1 = Dropout(0.5)(t1)
lstm_instance = LSTM(dense_size, implementation=1, name='rf_layer')
flair = lstm_instance(Permute((2, 1))(Reshape((dense_size, -1))(flair)))
t2 = lstm_instance(Permute((2, 1))(Reshape((dense_size, -1))(t2)))
t1 = lstm_instance(Permute((2, 1))(Reshape((dense_size, -1))(t1)))
else:
flair = Flatten()(flair)
t2 = Flatten()(t2)
t1 = Flatten()(t1)
flair = Dense(dense_size, activation='relu')(flair)
flair = Dropout(0.5)(flair)
t2 = concatenate([flair, t2])
t2 = Dense(dense_size, activation='relu')(t2)
t2 = Dropout(0.5)(t2)
t1 = concatenate([t2, t1])
t1 = Dense(dense_size, activation='relu')(t1)
t1 = Dropout(0.5)(t1)
tumor = Dense(2, activation='softmax', name='tumor')(flair)
core = Dense(3, activation='softmax', name='core')(t2)
enhancing = Dense(num_classes, activation='softmax', name='enhancing')(t1)
net = Model(inputs=merged_inputs, outputs=[tumor, core, enhancing])
# net_name_before = os.path.join(path,'baseline-brats2017.fold0.D500.f.p13.c3c3c3c3c3.n32n32n32n32n32.d256.e1.pad_valid.mdl')
# net = keras.models.load_model(net_name_before)
net.compile(optimizer='adadelta', loss='categorical_crossentropy', metrics=['accuracy'])
print(c['c'] + '[' + strftime("%H:%M:%S") + '] ' +
c['g'] + 'Training the model with a generator for ' +
c['b'] + '(%d parameters)' % net.count_params() + c['nc'])
print(net.summary())
net.fit_generator(
generator=load_patch_batch_train(
image_names=train_data,
label_names=train_labels,
centers=train_centers,
batch_size=batch_size,
size=patch_size,
# fc_shape = patch_size,
nlabels=num_classes,
dfactor=dfactor,
preload=preload,
split=not sequential,
datatype=np.float32
),
validation_data=load_patch_batch_train(
image_names=val_data,
label_names=val_labels,
centers=val_centers,
batch_size=batch_size,
size=patch_size,
# fc_shape = patch_size,
nlabels=num_classes,
dfactor=dfactor,
preload=preload,
split=not sequential,
datatype=np.float32
),
steps_per_epoch=train_steps_per_epoch,
validation_steps=val_steps_per_epoch,
max_q_size=queue,
epochs=epochs
)
net.save(net_name)
# Then we test the net.
for p, gt_name in zip(test_data, test_labels):
p_name = p[0].rsplit('/')[-2]
patient_path = '/'.join(p[0].rsplit('/')[:-1])
outputname = os.path.join(patient_path, 'deep-brats17' + sufix + 'test.nii.gz')
gt_nii = load_nii(gt_name)
gt = np.copy(gt_nii.get_data()).astype(dtype=np.uint8)
try:
load_nii(outputname)
except IOError:
roi_nii = load_nii(p[0])
roi = roi_nii.get_data().astype(dtype=np.bool)
centers = get_mask_voxels(roi)
test_samples = np.count_nonzero(roi)
image = np.zeros_like(roi).astype(dtype=np.uint8)
print(c['c'] + '[' + strftime("%H:%M:%S") + '] ' + c['g'] +
'<Creating the probability map ' + c['b'] + p_name + c['nc'] + c['g'] +
' (%d samples)>' % test_samples + c['nc'])
test_steps_per_epoch = -(-test_samples / batch_size)
y_pr_pred = net.predict_generator(
generator=load_patch_batch_generator_test(
image_names=p,
centers=centers,
batch_size=batch_size,
size=patch_size,
preload=preload,
),
steps=test_steps_per_epoch,
max_q_size=queue
)
[x, y, z] = np.stack(centers, axis=1)
if not sequential:
tumor = np.argmax(y_pr_pred[0], axis=1)
y_pr_pred = y_pr_pred[-1]
roi = np.zeros_like(roi).astype(dtype=np.uint8)
roi[x, y, z] = tumor
roi_nii.get_data()[:] = roi
roiname = os.path.join(patient_path, 'deep-brats17' + sufix + 'test.roi.nii.gz')
roi_nii.to_filename(roiname)
y_pred = np.argmax(y_pr_pred, axis=1)
image[x, y, z] = y_pred
# Post-processing (Basically keep the biggest connected region)
image = get_biggest_region(image)
labels = np.unique(gt.flatten())
results = (p_name,) + tuple([dsc_seg(gt == l, image == l) for l in labels[1:]])
text = 'Subject %s DSC: ' + '/'.join(['%f' for _ in labels[1:]])
print(text % results)
dsc_results.append(results)
print(c['g'] + ' -- Saving image ' + c['b'] + outputname + c['nc'])
roi_nii.get_data()[:] = image
roi_nii.to_filename(outputname)
def main(job_dir, **args):
##Setting up the path for saving logs
logs_path = job_dir + 'logs/tensorboard-{}'.format(int(time.time()))
logs_dir = job_dir + 'logs/tensorboard/'
##Using the GPU
with tf.device('/device:GPU:0'):
with file_io.FileIO(job_dir + 'dataset/train_data.pickle',
mode='rb') as file:
train_set = pickle.load(file)
with file_io.FileIO(job_dir + 'dataset/train_label.pickle',
mode='rb') as file:
train_label = pickle.load(file)
dense_layers = [0, 1, 2]
layer_sizes = [8, 16, 32, 64]
conv_layers = [1, 2, 3]
for dense_layer in dense_layers:
for layer_size in layer_sizes:
for conv_layer in conv_layers:
NAME = "{}-conv-{}-nodes-{}-dense-{}".format(
conv_layer, layer_size, dense_layer, int(time.time()))
model = Sequential()
model.add(
Conv2D(layer_size, (3, 3), input_shape=(64, 64, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
for l in range(conv_layer - 1):
model.add(Conv2D(layer_size, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
for _ in range(dense_layer):
model.add(Dense(layer_size))
model.add(Activation('relu'))
model.add(Dense(13))
model.add(Activation('sigmoid'))
tensorboard = callbacks.TensorBoard(log_dir=logs_dir +
"{}".format(NAME))
# checkpoint = ModelCheckpoint('model-checkpoint-{}.h5'.format(NAME),
# monitor='val_loss', verbose=1,
# save_best_only=True, mode='min')
model.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'],
)
model.fit(train_set,
train_label,
batch_size=40,
epochs=15,
validation_split=0.3,
callbacks=[tensorboard])
model.save('model-{}.h5'.format(NAME))
with file_io.FileIO('model-{}.h5'.format(NAME),
mode='r') as input_f:
with file_io.FileIO(job_dir + 'model/' +
'model-{}.h5'.format(NAME),
mode='w+') as output_f:
output_f.write(input_f.read())
def leave_one_cnn(cpgs, phenos, which_leave):
print(which_leave)
correct = 0
X = []
cpg = []
kids = []
for r in cpgs:
X.append(r[1])
cpg.append(r[0])
kids.append(r[2])
X = np.array(X)
X = X.reshape(num_kids, num_cpg)
kids = np.array(kids)
kids = kids.reshape(num_kids, num_cpg)
kids = np.transpose(kids).reshape(-1)
cpg = np.array(cpg[0:num_cpg])
kids = np.array(kids[0:num_kids])
Y = []
counter = 0
for r in phenos:
if (r[0] != kids[counter]):
print("kids don't match!")
exit()
counter += 1
Y.append(r[1])
#Y=np.mat(Y)
Y = np.asarray(Y)
X_test = np.asarray(X[which_leave])
Y_test = np.asarray([Y[which_leave]])
#leave one here
#print(X.shape)
#print(Y.shape)
X = np.vstack((X[:which_leave], X[(which_leave + 1):]))
t = Y[:which_leave]
t = np.expand_dims(t, axis=1)
tt = Y[(which_leave + 1):]
tt = np.expand_dims(tt, axis=1)
Y = np.vstack((t, tt))
# print(Y_test.shape)
# print(X_test.shape)
# print(X.shape)
# print(Y.shape)
sm = SMOTE(random_state=42)
#X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.1, random_state=42)
#X_res, Y_res = sm.fit_sample(X_train, Y_train)
X_res, Y_res = sm.fit_sample(X, Y)
"""
X_train, X_val, Y_train, Y_val = train_test_split(X_res,Y_res,test_size=0.05, random_state=42)
#X=np.transpose(X)
X_train = np.expand_dims(X_train, axis=2)
#X_train = np.expand_dims(X_train, axis=1)
X_val= np.expand_dims(X_val, axis=2)
"""
X_val = np.expand_dims(X, axis=2)
print(X.shape)
print(X_res.shape)
X_train = np.expand_dims(X_res, axis=2)
X_test = np.expand_dims(X_test, axis=1)
X_test = np.expand_dims(X_test, axis=0)
#Y_train=np.column_stack((Y_train,1-Y_train))
Y_train = np.column_stack((Y_res, 1 - Y_res))
#Y_val=np.column_stack((Y_val,1-Y_val))
Y_val = np.column_stack((Y, 1 - Y))
Y_test = np.column_stack((Y_test, 1 - Y_test))
#Y_res = np.expand_dims(Y_res, axis=1)
#np.column_stack((Y_res,1-Y_res))
#Y_test = np.expand_dims(Y_test, axis=1)
#print(X_res.shape)
#print(Y_res.shape)
#print(X_test.shape)
#print(Y_test.shape)
#deep feed forward
"""
model = Sequential((
Dense(1000,input_dim = num_cpg,activation='linear',use_bias=with_bias),
Dropout(dropout),
Dense(500,activation='linear',use_bias=with_bias),
Dropout(dropout),
Dense(300,activation='linear',use_bias=with_bias),
Dropout(dropout),
Dense(nb_class, activation='sigmoid'),
))
#deep cnn
"""
model = Sequential((
# The first conv layer learns `nb_filter` filters (aka kernels), each of size ``(filter_length, nb_input_series)``.
# Its output will have shape (None, window_size - filter_length + 1, nb_filter), i.e., for each position in
# the input timeseries, the activation of each filter at that position.
Convolution1D(nb_filter=nb_filter1,
filter_length=filter_length1,
activation='relu',
input_shape=(num_cpg, 1),
use_bias=with_bias),
MaxPooling1D(), # Downsample the output of convolution by 2X.
Dropout(dropout),
Convolution1D(nb_filter=nb_filter2,
filter_length=filter_length2,
activation='relu',
use_bias=with_bias),
MaxPooling1D(),
Dropout(dropout),
Flatten(),
Dense(
nb_filter2, activation='sigmoid'
), # For binary classification, change the activation to 'sigmoid'
Dense(
nb_class, activation='sigmoid'
), # For binary classification, change the activation to 'sigmoid'
Activation('softmax')))
adam = optimizers.Adam()
sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
earlyStopping = EarlyStopping(monitor='val_loss',
patience=30,
verbose=0,
mode='min')
mcp_save = ModelCheckpoint(save_path % (which_leave),
save_best_only=True,
monitor='val_loss',
mode='min')
reduce_lr_loss = ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=7,
verbose=1,
epsilon=1e-4,
mode='min')
# To perform (binary) classification instead:
# sgd is reported for better val accuracy: https://arxiv.org/abs/1705.08292
model.compile(loss='categorical_crossentropy',
optimizer=sgd,
metrics=['categorical_accuracy'])
#model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['binary_accuracy'])
#print('\n\nModel with input size {}, output size {}, {} conv filters of length {}'.format(model.input_shape,model.output_shape, nb_filter1, filter_length1))
#model.summary()
model.fit(X_train,
Y_train,
epochs=n_epoch,
batch_size=batch_size,
shuffle=True,
callbacks=[mcp_save],
verbose=0,
validation_data=(X_val, Y_val))
model.load_weights(filepath=save_path % (which_leave))
pred = model.predict(X_test)
#print(pred[0])
#print(Y_test[0])
if pred[0][0] < 0.5 and Y_test[0][0] == 0:
correct = 1
if pred[0][0] > 0.5 and Y_test[0][0] == 1:
correct = 1
print('\n\nactual', 'predicted', sep='\t')
for actual, predicted in zip(Y_test, pred.squeeze()):
print(actual.squeeze(), predicted, sep='\t')
if correct == 1:
print("correct!")
else:
print("false!")
#model.save(save_path%(which_leave))
print("save model to folder trained_model")
return ((pred[0][0], Y_test[0][0], correct))
def save_bottlebeck_features():
np.random.seed(2929)
vgg_model = applications.VGG16(weights='imagenet', include_top=False, input_shape=(150, 150, 3))
print('Model loaded.')
#initialise top model
top_model = Sequential()
top_model.add(Flatten(input_shape=vgg_model.output_shape[1:]))
top_model.add(Dense(256, activation='relu'))
top_model.add(Dropout(0.5))
top_model.add(Dense(1, activation='sigmoid'))
model = Model(inputs=vgg_model.input, outputs=top_model(vgg_model.output))
model.trainable = True
model.summary()
#Total of 20 layers. The classification is considered as one layer
#Therefore, intermediate is 19 layers
#0, 4[:4], 3[:7], 4[:11], 4[:15], 4[:19] (Group 0, 1, 2, 3, 4, 5)
#0 -> All trainable
#5 -> All non-trainable except classification layer
#Always keep layer 20 trainable because it is classification layer
#layer_count = 1
for layer in model.layers[:19]:
layer.trainable = False
#print("NO-Top: Layer is %d trainable" %layer_count)
#layer_count = layer_count + 1
model.summary()
train_datagen = ImageDataGenerator(rescale=1. / 255)
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_height, img_width),
batch_size=batch_size,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_height, img_width),
batch_size=batch_size,
class_mode='binary')
sgd = optimizers.Adam(lr=1e-6) #optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile( loss = "binary_crossentropy",
optimizer = sgd,
metrics=['accuracy']
)
# model.compile(optimizer='rmsprop',
# loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size,
verbose=1)
history_dict = history.history
#Plotting the training and validation loss
history_dict = history.history
loss_values = history_dict['loss']
val_loss_values = history_dict['val_loss']
epochs_0 = range(1, len(history_dict['acc']) + 1)
plt.plot(epochs_0, loss_values, 'bo', label='Training loss')
plt.plot(epochs_0, val_loss_values, 'b', label='Validation loss')
plt.title('ADvsNM_32_VGG16_Freeze_data4_group5 - Training and validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
#plt.show()
plt.savefig('ADvsNM_32_VGG16_Freeze_data4_group5_loss.png')
plt.close()
#Plotting the training and validation accuracy
acc_values = history_dict['acc']
val_acc_values = history_dict['val_acc']
plt.plot(epochs_0, acc_values, 'bo', label='Training acc')
plt.plot(epochs_0, val_acc_values, 'b', label='Validation acc')
plt.title('ADvsNM_32_VGG16_Freeze_data4_group5 - Training and validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
#plt.show()
plt.savefig('ADvsNM_32_VGG16_Freeze_data4_group5_acc.png')
plt.close()
X_train, X_test, y_train, y_test = train_test_split(x,
y,
test_size=0.2,
random_state=2)
###########################################################################################################################
# Custom_resnet_model_1
#Training the classifier alone
image_input = Input(shape=(224, 224, 3))
model = ResNet50(weights='imagenet',
input_tensor=image_input,
include_top=True)
model.summary()
last_layer = model.get_layer('avg_pool').output
x = Flatten(name='flatten')(last_layer)
out = Dense(num_classes, activation='softmax', name='output_layer')(x)
custom_resnet_model = Model(inputs=image_input, outputs=out)
custom_resnet_model.summary()
for layer in custom_resnet_model.layers[:-1]:
layer.trainable = False
custom_resnet_model.layers[-1].trainable
custom_resnet_model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
t = time.time()
hist = custom_resnet_model.fit(X_train,
def resnet_v1(input_shape, depth, num_classes=10):
"""ResNet Version 1 Model builder [a]
Stacks of 2 x (3 x 3) Conv2D-BN-ReLU
Last ReLU is after the shortcut connection.
At the beginning of each stage, the feature map size is halved (downsampled)
by a convolutional layer with strides=2, while the number of filters is
doubled. Within each stage, the layers have the same number filters and the
same number of filters.
Features maps sizes:
stage 0: 32x32, 16
stage 1: 16x16, 32
stage 2: 8x8, 64
The Number of parameters is approx the same as Table 6 of [a]:
ResNet20 0.27M
ResNet32 0.46M
ResNet44 0.66M
ResNet56 0.85M
ResNet110 1.7M
# Arguments
input_shape (tensor): shape of input image tensor
depth (int): number of core convolutional layers
num_classes (int): number of classes (CIFAR10 has 10)
# Returns
model (Model): Keras model instance
"""
if (depth - 2) % 6 != 0:
raise ValueError('depth should be 6n+2 (eg 20, 32, 44 in [a])')
# Start model definition.
num_filters = 16
num_res_blocks = int((depth - 2) / 6)
inputs = Input(shape=input_shape)
x = resnet_layer(inputs=inputs)
# Instantiate the stack of residual units
for stack in range(3):
for res_block in range(num_res_blocks):
strides = 1
if stack > 0 and res_block == 0: # first layer but not first stack
strides = 2 # downsample
y = resnet_layer(inputs=x,
num_filters=num_filters,
strides=strides)
y = resnet_layer(inputs=y,
num_filters=num_filters,
activation=None)
if stack > 0 and res_block == 0: # first layer but not first stack
# linear projection residual shortcut connection to match
# changed dims
x = resnet_layer(inputs=x,
num_filters=num_filters,
kernel_size=1,
strides=strides,
activation=None,
batch_normalization=False)
x = keras.layers.add([x, y])
x = Activation('relu')(x)
num_filters *= 2
# Add classifier on top.
# v1 does not use BN after last shortcut connection-ReLU
x = AveragePooling2D(pool_size=8)(x)
y = Flatten()(x)
outputs = Dense(num_classes,
activation='softmax',
kernel_initializer='he_normal')(y)
# Instantiate model.
model = Model(inputs=inputs, outputs=outputs)
return model
def get_model(args):
# Dataset config
assert args.dataset.lower() == 'avletters'
config = data_constants['avletters']
inputCNNshape = config['lstm_inputCNNshape']
inputMLPshape = config['lstm_inputMLPshape']
nb_classes = config['nb_classes']
# Build the CNN - pre-cross-connections
inputCNN = Input(shape=inputCNNshape)
inputNorm = TimeDistributed(Flatten())(inputCNN)
inputNorm = Masking(mask_value=0.)(inputNorm)
inputNorm = TimeDistributed(Reshape((80, 60, 1)))(inputNorm)
inputNorm = BatchNormalization(axis=1)(inputNorm)
conv = TimeDistributed(Convolution2D(8,
3,
3,
border_mode='same',
activation='relu'),
name='conv11')(inputNorm)
pool = TimeDistributed(MaxPooling2D((2, 2), strides=(2, 2)),
name='maxpool1')(conv)
# Build the MLP - pre-cross-connections
inputMLP = Input(shape=inputMLPshape)
inputMasked = Masking(mask_value=0., input_shape=inputMLPshape)(inputMLP)
fcMLP = TimeDistributed(Dense(32, activation='relu'),
name='fc1')(inputMasked)
# Add the 1st round of cross-connections - CNN to MLP
x21 = TimeDistributed(Convolution2D(8, 1, 1, border_mode='same'))(pool)
x21 = TimeDistributed(PReLU())(x21)
x21 = TimeDistributed(Flatten())(x21)
x21 = TimeDistributed(Dense(32))(x21)
x21 = TimeDistributed(PReLU())(x21)
# Add 1st shortcut (residual connection) from CNN input to MLP
short1_2dto1d = TimeDistributed(MaxPooling2D((4, 4),
strides=(4, 4)))(inputNorm)
short1_2dto1d = TimeDistributed(Flatten())(short1_2dto1d)
short1_2dto1d = TimeDistributed(Dense(32))(short1_2dto1d)
short1_2dto1d = TimeDistributed(PReLU())(short1_2dto1d)
# Cross-connections - MLP to CNN
x12 = TimeDistributed(Dense(25 * 15))(fcMLP)
x12 = TimeDistributed(PReLU())(x12)
x12 = TimeDistributed(Reshape((25, 15, 1)))(x12)
x12 = TimeDistributed(Conv2DTranspose(8, (16, 16), padding='valid'))(x12)
x12 = TimeDistributed(PReLU())(x12)
# 1st shortcut (residual connection) from MLP input to CNN
short1_1dto2d = TimeDistributed(Dense(25 * 15))(inputMasked)
short1_1dto2d = TimeDistributed(PReLU())(short1_1dto2d)
short1_1dto2d = TimeDistributed(Reshape((25, 15, 1)))(short1_1dto2d)
short1_1dto2d = TimeDistributed(
Conv2DTranspose(8, (16, 16), padding='valid'))(short1_1dto2d)
short1_1dto2d = TimeDistributed(PReLU())(short1_1dto2d)
# CNN - post-cross-connections 1
pool = add([pool, short1_1dto2d])
merged = concatenate([pool, x12])
conv = TimeDistributed(Convolution2D(16,
3,
3,
border_mode='same',
activation='relu'),
name='conv21')(merged)
pool = TimeDistributed(MaxPooling2D((2, 2), strides=(2, 2)),
name='maxpool2')(conv)
# MLP - post-cross-connections 1
fcMLP = add([fcMLP, short1_2dto1d])
fcMLP = concatenate([fcMLP, x21])
fcMLP = TimeDistributed(Dense(32, activation='relu'), name='fc2')(fcMLP)
# Add the 2nd round of cross-connections - CNN to MLP
x21 = TimeDistributed(Convolution2D(16, 1, 1, border_mode='same'))(pool)
x21 = TimeDistributed(PReLU())(x21)
x21 = TimeDistributed(Flatten())(x21)
x21 = TimeDistributed(Dense(64))(x21)
x21 = TimeDistributed(PReLU())(x21)
# Add 2nd shortcut (residual connection) from CNN input to MLP
short2_2dto1d = TimeDistributed(MaxPooling2D((8, 8),
strides=(8, 4)))(inputNorm)
short2_2dto1d = TimeDistributed(Flatten())(short2_2dto1d)
short2_2dto1d = TimeDistributed(Dense(32))(short2_2dto1d)
short2_2dto1d = TimeDistributed(PReLU())(short2_2dto1d)
# Cross-connections - MLP to CNN
x12 = TimeDistributed(Dense(13 * 8))(fcMLP)
x12 = TimeDistributed(PReLU())(x12)
x12 = TimeDistributed(Reshape((13, 8, 1)))(x12)
x12 = TimeDistributed(Conv2DTranspose(16, (8, 8), padding='valid'))(x12)
x12 = TimeDistributed(PReLU())(x12)
# 2nd shortcut (residual connection) from MLP input to CNN
short2_1dto2d = TimeDistributed(Dense(13 * 8))(inputMasked)
short2_1dto2d = TimeDistributed(PReLU())(short2_1dto2d)
short2_1dto2d = TimeDistributed(Reshape((13, 8, 1)))(short2_1dto2d)
short2_1dto2d = TimeDistributed(
Conv2DTranspose(16, (8, 8), padding='valid'))(short2_1dto2d)
short2_1dto2d = TimeDistributed(PReLU())(short2_1dto2d)
# CNN - post-cross-connections 2
pool = add([pool, short2_1dto2d])
merged = concatenate([pool, x12])
reshape = TimeDistributed(Flatten(), name='flatten1')(merged)
fcCNN = TimeDistributed(Dense(64, activation='relu'),
name='fcCNN')(reshape)
# Merge the models
fcMLP = add([fcMLP, short2_2dto1d])
merged = concatenate([fcCNN, fcMLP, x21])
merged = BatchNormalization(axis=1, name='mergebn')(merged)
merged = Dropout(0.5, name='mergedrop')(merged)
lstm = LSTM(64)(merged)
out = Dense(nb_classes, activation='softmax')(lstm)
# Return the model object
model = Model(input=[inputCNN, inputMLP], output=out)
return model
enc = BatchNormalization()(enc)
enc = Conv2D(filters=128,
kernel_size=5,
strides=(2, 2),
padding='same',
activation='relu',
name='encoder_second_conv')(enc)
enc = BatchNormalization()(enc)
enc = Conv2D(filters=256,
kernel_size=5,
strides=(2, 2),
padding='same',
activation='relu',
name='encoder_third_conv')(enc)
enc = BatchNormalization()(enc)
enc = Flatten()(enc)
z_mean = Dense(units=2048, activation='relu', name='encoder_output_mean')(enc)
z_mean = BatchNormalization()(z_mean)
z_log_var = Dense(units=2048, name='encoder_output_log_var')(enc)
z_log_var = BatchNormalization()(z_log_var)
z_sampled = Lambda(sampling, name='sampler')([z_mean, z_log_var])
encoder = Model(encoder_input, [z_mean, z_log_var, z_sampled], name='encoder')
encoder.summary()
#making the decoder model
decoder_input = Input(shape=(2048, ), name='decoder_input')
dec = Dense(units=8 * 8 * 256, activation='relu',
name='decoder_first_layer')(decoder_input)
dec = BatchNormalization()(dec)
dec = Reshape((8, 8, 256))(dec)
dec = Conv2DTranspose(filters=256,
def resnet50_model(img_rows, img_cols, color_type=1, num_classes=None):
"""
Resnet 50 Model for Keras
Model Schema is based on
https://github.com/fchollet/deep-learning-models/blob/master/resnet50.py
ImageNet Pretrained Weights
https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_th_dim_ordering_th_kernels.h5
Parameters:
img_rows, img_cols - resolution of inputs
channel - 1 for grayscale, 3 for color
num_classes - number of class labels for our classification task
"""
# Handle Dimension Ordering for different backends
global bn_axis
if K.image_dim_ordering() == 'tf':
bn_axis = 3
img_input = Input(shape=(img_rows, img_cols, color_type))
else:
bn_axis = 1
img_input = Input(shape=(color_type, img_rows, img_cols))
x = ZeroPadding2D((3, 3))(img_input)
x = Convolution2D(64, 7, 7, subsample=(2, 2), name='conv1')(x)
x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
x = Activation('relu')(x)
x = MaxPooling2D((3, 3), strides=(2, 2))(x)
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
# Fully Connected Softmax Layer
x_fc = AveragePooling2D((7, 7), name='avg_pool')(x)
x_fc = Flatten()(x_fc)
x_fc = Dense(1000, activation='softmax', name='fc1000')(x_fc)
# Create model
model = Model(img_input, x_fc)
# Load ImageNet pre-trained data
if K.image_dim_ordering() == 'th':
# Use pre-trained weights for Theano backend
weights_path = 'imagenet_models/resnet50_weights_th_dim_ordering_th_kernels.h5'
else:
# Use pre-trained weights for Tensorflow backend
weights_path = 'imagenet_models/resnet50_weights_tf_dim_ordering_tf_kernels.h5'
model.load_weights(weights_path)
# Truncate and replace softmax layer for transfer learning
# Cannot use model.layers.pop() since model is not of Sequential() type
# The method below works since pre-trained weights are stored in layers but not in the model
x_newfc = AveragePooling2D((7, 7), name='avg_pool')(x)
x_newfc = Flatten()(x_newfc)
x_newfc = Dense(num_classes, activation='softmax', name='fc10')(x_newfc)
# Create another model with our customized softmax
model = Model(img_input, x_newfc)
# Learning rate is changed to 0.001
sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd,
loss='categorical_crossentropy',
metrics=['accuracy', metrics.categorical_accuracy])
return model
def buildModel(self):
"""Model layers"""
# character input
character_input = Input(shape=(
None,
52,
), name="Character_input")
embed_char_out = TimeDistributed(
Embedding(len(self.char2Idx),
30,
embeddings_initializer=RandomUniform(minval=-0.5,
maxval=0.5)),
name="Character_embedding")(character_input)
dropout = Dropout(self.dropout)(embed_char_out)
# CNN, time distributed allows us to map each "time stamp", i.e. character in a sentence, to an embedding.
# Without time distributed the mapping would be between
conv1d_out = TimeDistributed(Conv1D(kernel_size=self.conv_size,
filters=30,
padding='same',
activation='tanh',
strides=1),
name="Convolution")(dropout)
maxpool_out = TimeDistributed(MaxPooling1D(52),
name="Maxpool")(conv1d_out)
char = TimeDistributed(Flatten(), name="Flatten")(maxpool_out)
char = Dropout(self.dropout)(char)
# word-level input
words = Input(shape=(
None,
600,
), dtype='float32', name='words_input')
# words_input = Input(shape=(None,), dtype='int32', name='words_input')
# words = Embedding(input_dim=self.wordEmbeddings.shape[0], output_dim=self.wordEmbeddings.shape[1],
# weights=[self.wordEmbeddings],
# trainable=False)(words_input)
# case-info input
casing_input = Input(shape=(None, ),
dtype='int32',
name='casing_input')
casing = Embedding(output_dim=self.caseEmbeddings.shape[1],
input_dim=self.caseEmbeddings.shape[0],
weights=[self.caseEmbeddings],
trainable=False)(casing_input)
# concat & BLSTM
output = concatenate([words, casing, char])
output = Bidirectional(
LSTM(
self.lstm_state_size,
return_sequences=True,
dropout=self.dropout, # on input to each LSTM block
recurrent_dropout=self.
dropout_recurrent # on recurrent input signal
),
name="BLSTM")(output)
output = TimeDistributed(Dense(len(self.label2Idx),
activation='softmax'),
name="Softmax_layer")(output)
# set up model
self.model = Model(inputs=[words, casing_input, character_input],
outputs=[output])
#self.model = Model(inputs=[words_input, casing_input, character_input], outputs=[output])
self.model.compile(loss='sparse_categorical_crossentropy',
optimizer=self.optimizer)
self.init_weights = self.model.get_weights()
plot_model(self.model, to_file='model.png')
print("Model built. Saved model.png\n")
def train_model(batch_size=1000,
epochs=1005,
num_samples=10000,
var1=0.99,
var2=0.01):
num_classes = 10
print('var1:', var1)
print('var2:', var2)
# input image dimensions
img_rows, img_cols = 28, 28
# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
x_train = x_train[0:num_samples]
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)[0:num_samples]
y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(Flatten(input_shape=(28, 28, 1)))
model.add(Dense(64, activation='tanh'))
model.add(Dense(64, activation='tanh'))
model.add(Dense(64, activation='tanh'))
model.add(Dense(64, activation='tanh'))
model.add(Dense(64, activation='tanh'))
model.add(Dense(64, activation='tanh'))
model.add(Dense(64, activation='tanh'))
model.add(Dense(64, activation='tanh'))
model.add(Dense(64, activation='tanh'))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.sgd(),
metrics=['accuracy'])
model.summary()
n_pop = 10
n_para = 84170
fit = np.zeros((n_pop, 1))
pop = np.random.normal(0, 0.01, (n_pop, n_para))
count_update = 0
flag_add = 0
flag_stop = 0
minibatches = random_mini_batches(x_train, y_train, batch_size)
len_batch = len(minibatches)
record = np.zeros((epochs, 4))
count_non_update = 0
for i in range(epochs):
print(i,
'th iteration------------------------------------------------')
count_batches = 0
for minibatch in minibatches:
(minibatch_X, minibatch_Y) = minibatch
model.train_on_batch(minibatch_X, minibatch_Y)
for n in range(10):
a = np.array(model.get_weights())
score_old = model.evaluate(minibatch_X, minibatch_Y,
verbose=0)[0]
b = np.array(model.get_weights())
if flag_add == 0:
j = np.random.randint(0, len(b))
adding_random = np.random.normal(0, 0.01, b[j].shape)
b[j] = b[j] + adding_random
model.set_weights(b)
score_new = model.evaluate(minibatch_X, minibatch_Y,
verbose=0)[0]
if score_new < score_old * (var1 + i * var2 / epochs):
a = copy.deepcopy(b)
score_old = score_new
count_update = count_update + 1
flag_add = 1
break
else:
model.set_weights(a)
flag_add = 0
count_batches = count_batches + 1
score = model.evaluate(x_train, y_train, verbose=0)
# with K.Session() as sess:
#
# outputTensor = model.output # Or model.layers[index].output
# listOfVariableTensors = model.trainable_weights
# gradients =K.gradients(outputTensor, listOfVariableTensors)
record[i, 0], record[i, 1] = model.evaluate(x_train,
y_train,
verbose=0)
record[i, 2], record[i, 3] = model.evaluate(x_test, y_test, verbose=0)
print('Train loss:', record[i, 0])
print('Train accuracy:', record[i, 1])
print('Test loss:', record[i, 2])
print('Test accuracy', record[i, 3])
print('count_update:', count_update)
if i > 999 and i % 500 == 0:
df = pd.DataFrame(
record,
columns=['train_loss', 'train_acc', 'test_loss', 'test_acc'])
df.to_csv('minist_ga' + str(i) + str(num_samples) + str(var1) +
str(var2) + '.csv',
index=None,
header=True)
resnet_input = Input(shape=(224,224,3))
resnet_model = ResNet50(weights='imagenet', include_top=False, input_tensor=resnet_input)
"""net = resnet_model.get_layer('activation_46').output
net = MaxPooling2D((7, 7), name='max_pool')(net)
net = Flatten(name='flatten')(net)
p_drop = 0.5
net = Dense(512, name='fc1')(net)
#net = BatchNormalization()(net)
net = PReLU()(net)
net = Dropout(rate=p_drop)(net)"""
net = resnet_model.output
net = Flatten(name='flatten')(net)
net = Dense(512, activation='relu', name='fc1')(net)
net = Dense(512, name='embded')(net)
net = Lambda(l2Norm, output_shape=[512])(net)
base_model = Model(resnet_model.input, net, name='resnet_model')
base_model.summary()
""" Train just the new layers, let the pretrained ones be as they are (they'll be trained later) """
for layer in resnet_model.layers:
layer.trainable = False
""" Building triple siamese architecture """
def ResNetPreAct(input_shape=(500,500,3), nb_classes=13, layer1_params=(5,64,2), res_layer_params=(3,16,3),
final_layer_params=None, init='glorot_normal', reg=0.0, use_shortcuts=False):
"""
Return a new Residual Network using full pre-activation based on the work in
"Identity Mappings in Deep Residual Networks" by He et al
http://arxiv.org/abs/1603.05027
The following network definition achieves 92.0% accuracy on CIFAR-10 test using
`adam` optimizer, 100 epochs, learning rate schedule of 1e.-3 / 1.e-4 / 1.e-5 with
transitions at 50 and 75 epochs:
ResNetPreAct(layer1_params=(3,128,2),res_layer_params=(3,32,25),reg=reg)
Removed max pooling and using just stride in first convolutional layer. Motivated by
"Striving for Simplicity: The All Convolutional Net" by Springenberg et al
(https://arxiv.org/abs/1412.6806) and my own experiments where I observed about 0.5%
improvement by replacing the max pool operations in the VGG-like cifar10_cnn.py example
in the Keras distribution.
Parameters
----------
input_dim : tuple of (C, H, W)
nb_classes: number of scores to produce from final affine layer (input to softmax)
layer1_params: tuple of (filter size, num filters, stride for conv)
res_layer_params: tuple of (filter size, num res layer filters, num res stages)
final_layer_params: None or tuple of (filter size, num filters, stride for conv)
init: type of weight initialization to use
reg: L2 weight regularization (or weight decay)
use_shortcuts: to evaluate difference between residual and non-residual network
"""
sz_L1_filters, nb_L1_filters, stride_L1 = layer1_params
sz_res_filters, nb_res_filters, nb_res_stages = res_layer_params
use_final_conv = (final_layer_params is not None)
if use_final_conv:
sz_fin_filters, nb_fin_filters, stride_fin = final_layer_params
sz_pool_fin = input_shape[1] / (stride_L1 * stride_fin)
else:
sz_pool_fin = input_shape[1] / (stride_L1)
'''
from keras import backend as K
# Permute dimension order if necessary
if K.image_dim_ordering() == 'tf':
input_shape = (input_shape[1], input_shape[2], input_shape[0])
'''
img_input = Input(shape=input_shape, name='cifar')
x = Conv2D(
filters=nb_L1_filters,
kernel_size=(sz_L1_filters,sz_L1_filters),
padding='same',
strides=(stride_L1, stride_L1),
kernel_initializer=init,
kernel_regularizer=l2(reg),
use_bias=False,
name='conv0'
)(img_input)
x = BatchNormalization(axis=3, name='bn0')(x)
x = Activation('relu', name='relu0')(x)
for stage in range(1,nb_res_stages+1):
x = rnpa_bottleneck_layer(
x,
(nb_L1_filters, nb_res_filters),
sz_res_filters,
stage,
init=init,
reg=reg,
use_shortcuts=use_shortcuts
)
x = BatchNormalization(axis=3, name='bnF')(x)
x = Activation('relu', name='reluF')(x)
if use_final_conv:
x = Conv2D(
filters=nb_L1_filters,
kernel_size=(sz_L1_filters,sz_L1_filters),
padding='same',
strides=(stride_fin, stride_fin),
kernel_initializer=init,
kernel_regularizer=l2(reg),
name='convF'
)(x)
x = AveragePooling2D((sz_pool_fin,sz_pool_fin), name='avg_pool')(x)
# x = Flatten(name='flat')(x)
x = Flatten()(x)
x = Dense(nb_classes, activation='softmax', name='fc10')(x)
return Model(img_input, x, name='rnpa')
l_in = Input(shape=(DIM_COM,), dtype='float32', name='l'); # inputed command; o_in = Input(shape=(DIM_IMG,DIM_IMG,1), dtype='float32', name='o'); # inputed image; a_in = Input(shape=(DIM_a,), dtype='float32', name='a_last'); # last step action; Gs_in = Input(shape=(DIM_Gs,), dtype='float32', name='Gs'); # G_s[t]; Gs_in_ = Input(shape=(DIM_Gs,), dtype='float32', name='Gs_'); # G_s[t-1]; Gi_in = Input(shape=(DIM_Gi,), dtype='float32', name='Gi'); # G_i[t]; #------- Embedding ------- T = Dense(32, activation='relu')(l_in); #------- CNN ------------- C = Conv2D(16, (5,5), strides=(1, 1), activation='relu',padding='same')(o_in); # out:60*60 *6; C = MaxPooling2D(pool_size=(2, 2), strides=(2, 2),padding='same')(C); # out:30*30 *6; C = Conv2D(8, (3,3), strides=(1, 1), activation='relu',padding='same')(C); # out:60*60 *6; C = MaxPooling2D(pool_size=(2, 2), strides=(2, 2),padding='same')(C); C = Conv2D(8, (3,3), strides=(1, 1), activation='relu',padding='same')(C); C = MaxPooling2D(pool_size=(2, 2), strides=(2, 2),padding='same')(C); C = Flatten()(C); # the output cnn features; #------- R(V,l) ------------- #C_ = concatenate([C,T]); # the output cat; C_1 = Dense(32, activation='relu')(C); C_2 = Dense(32, activation='relu')(T); C_ = Add()([C_1,C_2]); R_S = Dense(DIM_Gs, activation='relu')(C_); # the output GFT; #------ I(V,l,G_s[t],G_s[t-1]) ---- C_i = Dense(DIM_hm, activation='relu')(C_); S_i = Dense(DIM_hm, activation='relu')(Gs_in); S_i_ = Dense(DIM_hm, activation='relu')(Gs_in_); G_i = Add()([C_i,S_i,S_i_]); G_i = Dense(32, activation='relu')(G_i); I_G = Dense(DIM_Gi, activation='relu')(G_i); #------ GRU for Pi(G_s,G_i,C*) ------- G_S = Embedding(output_dim=ACT_OUT_DIM, input_dim=DIM_Gs, input_length=ACT_STEPS,name = 'emb1')(Gs_in);
def main():
####### Some Parameters ###########
nepochs =int(argv[1])
# Energy cuts
cutlow=40
cuthigh=260
# 2 capas ocultas con numero de neuronas, definidas en la variable siguiente
#neurons = [128, 32, 8]
#nneurons = len(argv)-3
neurons = [int(argv[2]),int(argv[3]),int(argv[4])]
print("neurons: ",argv[2],argv[3],argv[4])
nepochstot = nepochs
modelloaded = False
####### Estructura de la FFNN ###########
####### Comprobamos si la cargamos o la creamos. Si la cargamos lo hacemos ahora, si la creamos lo haremos luego.
if( len(argv)==7 and argv[5]=='load'):
ifolder = "/tmp/ml_outputs/testsize0.25sizeofbatch0.75"
filename = "%s/model_ANN_epochs%d_2HL_%d_%d_%d__%d_%d.h5" % (ifolder,nepochs,neurons[0],neurons[1],neurons[2],cutlow,cuthigh)
#filename = "%s" % (ifile)
print('Loading model : ',filename)
model = load_model(filename)
modelloaded = True
# epochs that want to be run in this iteration.
nepochs = int(argv[6])
# total number of epochs that the ouput model has gone through
nepochstot += nepochs
####### Estructura de la FFNN ###########
####### Reading the data ###########
#data = pd.read_csv('../rawdata/sample.txt', sep="\t", header=None)
#data = pd.read_csv('../rawdata/lightpatternXYZar40_nominal_30000.txt', sep="\t", header=None)
data01 = pd.read_csv('/tmp/data/sk_ar40_0_100.txt', sep="\t", header=None)
data02 = pd.read_csv('/tmp/data/sk_ar40_100_200.txt', sep="\t", header=None)
data03 = pd.read_csv('/tmp/data/sk_ar40_200_300.txt', sep="\t", header=None)
data04 = pd.read_csv('/tmp/data/sk_ar40_300_400.txt', sep="\t", header=None)
data05 = pd.read_csv('/tmp/data/sk_ar40_400_500.txt', sep="\t", header=None)
data06 = pd.read_csv('/tmp/data/sk_ar40_500_600.txt', sep="\t", header=None)
data07 = pd.read_csv('/tmp/data/sk_ar40_600_700.txt', sep="\t", header=None)
data08 = pd.read_csv('/tmp/data/sk_ar40_700_800.txt', sep="\t", header=None)
data09 = pd.read_csv('/tmp/data/sk_ar40_800_900.txt', sep="\t", header=None)
data10 = pd.read_csv('/tmp/data/sk_ar40_900_1000.txt', sep="\t", header=None)
data1 = pd.concat([data01,data02,data03,data04,data05,data06,data07,data08,data09,data10], ignore_index=True)
data1 = shuffle(data1)
data2 = pd.read_csv('/tmp/data/sk_neck_0_500.txt', sep="\t", header=None)
data3 = pd.read_csv('/tmp/data/sk_neck_500_1000.txt', sep="\t", header=None)
data4 = pd.read_csv('/tmp/data/sk_neck_1000_1500.txt', sep="\t", header=None)
data5 = pd.read_csv('/tmp/data/sk_neck_1500_2000.txt', sep="\t", header=None)
datan = pd.concat([data2,data3,data4,data5], ignore_index=True)
print(shape(data1))
print(shape(datan))
# Adding summed Charge column
datan['sumPMTs']=datan.iloc[:,0:255].sum(axis=1)
# energy cuts: Selecting data of neck events
datan = datan[(datan['sumPMTs'] >= cutlow) & (datan['sumPMTs'] <= cuthigh)]
print('neck aftercuts ',shape(datan))
data1['sumPMTs']=data1.iloc[:,0:255].sum(axis=1)
# energy cuts: Performing same cuts on neck and Ar events
data1 = data1[(data1['sumPMTs'] >= cutlow) & (data1['sumPMTs'] <= cuthigh)]
print('LAr aftercuts ',shape(data1))
# Cutting Ar-40 data to avoid biasing
datanum = datan.shape[0]*3
print('LAr accepted ',datanum)
datag= data1[:datanum]
data = pd.concat([datag,datan], ignore_index=True)
data = shuffle(data)
# data = data[(data['sumPMTs'] >= cutlow) & (data['sumPMTs'] <= cuthigh)]
print(shape(data))
print(data.columns)
# Selecting the last column: 1 if neck, 0 if Ar-40 recoil
Y= data.iloc[:,513:514]
print("Y.shape", Y.shape)
# Enconde binary option A:
#Y = to_categorical(Y,num_classes=2)
#print("Y", Y.shape)
# Encode binary option B: class values as integers
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)
print("Y",Y)
X= data.iloc[:,0:255]
print(X.shape)
pos= data.iloc[:,510:513]
mbpos= data.iloc[:,514:517]
print(pos.shape)
X = np.asarray(X)
Y = np.asarray(Y)
pos = np.asarray(pos)
mbpos = np.asarray(mbpos)
# Creamos los conjuntos de datos de entrenamiento y de evaluacion.
#test_size = 100
test_size = int(np.floor(0.25*X.shape[0]) )
#sizeofbatch =int(np.floor(0.25*test_size))
sizeofbatch =int(np.floor(0.75*test_size))
trainX, testX = X[:-test_size], X[-test_size:]
#trainX = np.reshape(trainX, (1, trainX.shape[0], trainX.shape[1]))
#testX = np.reshape(testX, (1, testX.shape[0], testX.shape[1]))
trainY, testY = Y[:-test_size], Y[-test_size:]
#trainY = np.reshape(trainY, (1, trainY.shape[0], trainY.shape[1]))
#testYX = np.reshape(testY, (1, testY.shape[0], testY.shape[1]))
testpos = pos[-test_size:]
mbtestpos = mbpos[-test_size:]
chargeT=data['sumPMTs']
chargeT=chargeT[-test_size:]
print("lengths:", len(chargeT), len(testY))
#trainX=trainX[1:]
#trainY=trainY[1:]
trainX = np.reshape(trainX, trainX.shape + (1,))
testX = np.reshape(testX, testX.shape + (1,))
print(trainX.shape, testX.shape,trainY.shape, testY.shape)
print("trainX.shape", trainX.shape[0], trainX.shape[1])
print("trainY.shape", trainY.shape[0], trainY.shape[1])
####### Estructura de la FFNN ###########
####### Si no está cargada la creamos ###########
if (not modelloaded):
print('Loading model')
# 2 capas ocultas con numero de neuronas, definidas en la variable siguiente
#neurons = [128, 32, 8]
#nneurons = len(argv)-3
#neurons = [int(argv[2]),int(argv[3]),int(argv[4])]
# print("neurons: ",argv[2],argv[3],argv[4])
# Creamos la base del modelo
model = Sequential()
# Ponemos una primera capa oculta
model.add(Dense(neurons[0], activation='relu', input_shape=(trainX.shape[1], 1)))
#print(model.layers[-1].output_shape)
# Incorporamos una segunda capa oculta
model.add(Dense(neurons[1], activation='relu'))
#print(model.layers[-1].output_shape)
# Incorporamos una terceraa capa oculta
model.add(Dense(neurons[2], activation='relu'))
# Aplanamos los datos para reducir la dimensionalidad en la salida
model.add(Flatten())
# A\~nadimos la capa de salida de la red con activacion lineal
#model.add(Dense( trainY.shape[1], activation='tanh'))
model.add(Dense( trainY.shape[1], activation='sigmoid'))
# Compilamos el modelo usando el optimizador Adam
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
#model.compile(loss="binary_crossentropy", optimizer="adam", metrics=['binary_crossentropy'])
print(model.layers[-1].output_shape)
# Entrenamos la red
#model.fit(trainX, trainY, epochs=nepochs, batch_size=1, verbose=2)
history=model.fit(trainX, trainY, epochs=nepochs, batch_size=sizeofbatch, validation_data=(testX, testY), verbose=2)
# Pronosticos
pred = model.predict(testX)
print("pred.shape: ",pred.shape)
print("testY.shape: ",testY.shape)
errors= np.empty([test_size])
#print('\n\nReal', ' ', 'Pronosticado')
#for actual, predicted in zip(testY, pred.squeeze()):
#print(actual.squeeze(), '\t', predicted, '\t',actual.squeeze()-predicted)
R= np.empty([test_size])
mbR= np.empty([test_size])
print(R.shape)
print('sumPMT format ',chargeT.shape)
filename = "./r_error_ANN_epochs%d_2HL_%d_%d_%d__%d_%d_binaryclass.txt" % (nepochstot, neurons[0],neurons[1],neurons[2],cutlow,cuthigh)
fh = open(filename, "w")
#errors.append(actual.squeeze()-predicted)
for i in range(test_size):
#print("X:", testY[i,0],pred[i,0], testY[i,0]-pred[i,0], "\t Y:", testY[i,1],pred[i,1],testY[i,1]-pred[i,1], "\t Z:")
errors[i]= testY[i]-pred[i]
#errorsY[i]= testY[i,1]-pred[i,1]
#errorsZ[i]= testY[i,2]-pred[i,2]
R[i]= np.sqrt(testpos[i][0]*testpos[i][0]+testpos[i][1]*testpos[i][1]+testpos[i][2]*testpos[i][2])
mbR[i]= np.sqrt(mbtestpos[i][0]*mbtestpos[i][0]+mbtestpos[i][1]*mbtestpos[i][1]+mbtestpos[i][2]*mbtestpos[i][2])
#print(chargeT.iloc[i])
#print(float(chargeT.iloc[i]))
#print(Y[i])
#print(pred[i])
myline = "%f %d %f %f %f\n" % (R[i], testY[i], pred[i], float(chargeT.iloc[i]), mbR[i])
#myline = "%f %d %f %f\n" % (R[i], testY[i], pred[i], float(chargeT.iloc[i]))
fh.write(myline)
fh.close()
# Guardar el modelo, si se quiere
filename = "./model_ANN_epochs%d_2HL_%d_%d_%d__%d_%d_binaryclass.h5" % (nepochstot, neurons[0],neurons[1],neurons[2],cutlow,cuthigh)
model.save(filename)
# Calcular ECM y EAM
testScoreECM = mean_squared_error(testY, pred)
print('ECM: %.4f' % (testScoreECM))
testScoreEAM = mean_absolute_error(testY, pred)
print('EAM: %.4f' % (testScoreEAM))
# histogram errors
fig,ax = plt.subplots(nrows=1,ncols=1,figsize=(8,8))# 6,6
plt.figure(1)
#plt.ylim(0,500)
ax.set_yscale("log")
plt.hist(errors, 40)
plt.title('Error X')
filename = "./histerrors_ANN_epochs%d_2HL_%d_%d_%d__%d_%d_binaryclass.eps" % (nepochstot, neurons[0],neurons[1],neurons[2],cutlow,cuthigh)
plt.savefig(filename)
# summarize history for loss
fig,ax = plt.subplots(nrows=1,ncols=1,figsize=(8,8))# 6,6
plt.figure(2)
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
ax.set_yscale("log")
#plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
filename = "./loss_ANN_epochs%d_2HL_%d_%d_%d__%d_%d_binaryclass.eps" % (nepochstot, neurons[0],neurons[1],neurons[2],cutlow,cuthigh)
plt.savefig(filename)
fig,ax = plt.subplots(nrows=1,ncols=1,figsize=(8,8))# 6,6
plt.figure(3)
plt.scatter(R, pred, s=0.1, color='black')
plt.scatter(R, testY, s=0.1, color='red')
#plt.title('model loss')
plt.ylabel('IsNeck')
plt.xlabel('radius')
plt.ylim(-1.1,1.1)
plt.legend(['train', 'test'], loc='upper left')
filename = "./scatterplotr_ANN_epochs%d_2HL_%d_%d_%d__%d_%d_binaryclass.eps" % (nepochstot, neurons[0],neurons[1],neurons[2],cutlow,cuthigh)
plt.savefig(filename)
fig,ax = plt.subplots(nrows=1,ncols=1,figsize=(8,8))# 6,6
plt.figure(4)
plt.scatter( testY, pred, s=0.1, color='black')
plt.ylabel('Pred')
plt.xlabel('True')
plt.xlim(-0.1,1.1)
plt.legend(['pred', 'test'], loc='upper left')
filename = "./scatterplot_TruevsPred_ANN_epochs%d_2HL_%d_%d_%d__%d_%d_binaryclass.eps" % (nepochstot, neurons[0],neurons[1],neurons[2],cutlow,cuthigh)
plt.savefig(filename)
fig,ax = plt.subplots(nrows=1,ncols=1,figsize=(8,8))# 6,6
plt.figure(5)
plt.scatter(chargeT, errors, s=0.1, color='black')
#plt.scatter(sumPMTs, testY, s=0.1, color='red')
#plt.title('model loss')
plt.ylabel('error')
plt.xlabel('charge')
plt.ylim(-1.1,1.1)
plt.legend(['train', 'test'], loc='upper left')
filename = "./scatterplotch_ANN_epochs%d_2HL_%d_%d_%d__%d_%d_binaryclass.eps" % (nepochstot, neurons[0],neurons[1],neurons[2],cutlow,cuthigh)
plt.savefig(filename)
def ssd300(input_shape=(300, 300, 3),
num_classes=80,
include_top=True,
weight_decay=0.):
net = {}
# Block 1
input_tensor = Input(shape=input_shape)
img_size = (input_shape[1], input_shape[0])
net['input'] = input_tensor
net['conv1_1'] = Convolution2D(64,
3,
3,
activation='relu',
W_regularizer=l2(weight_decay),
border_mode='same',
name='conv1_1')(net['input'])
net['conv1_2'] = Convolution2D(64,
3,
3,
activation='relu',
W_regularizer=l2(weight_decay),
border_mode='same',
name='conv1_2')(net['conv1_1'])
net['pool1'] = MaxPooling2D((2, 2),
strides=(2, 2),
border_mode='same',
name='pool1')(net['conv1_2'])
# Block 2
net['conv2_1'] = Convolution2D(128,
3,
3,
activation='relu',
W_regularizer=l2(weight_decay),
border_mode='same',
name='conv2_1')(net['pool1'])
net['conv2_2'] = Convolution2D(128,
3,
3,
activation='relu',
W_regularizer=l2(weight_decay),
border_mode='same',
name='conv2_2')(net['conv2_1'])
net['pool2'] = MaxPooling2D((2, 2),
strides=(2, 2),
border_mode='same',
name='pool2')(net['conv2_2'])
# Block 3
net['conv3_1'] = Convolution2D(256,
3,
3,
activation='relu',
W_regularizer=l2(weight_decay),
border_mode='same',
name='conv3_1')(net['pool2'])
net['conv3_2'] = Convolution2D(256,
3,
3,
activation='relu',
W_regularizer=l2(weight_decay),
border_mode='same',
name='conv3_2')(net['conv3_1'])
net['conv3_3'] = Convolution2D(256,
3,
3,
activation='relu',
W_regularizer=l2(weight_decay),
border_mode='same',
name='conv3_3')(net['conv3_2'])
net['pool3'] = MaxPooling2D((2, 2),
strides=(2, 2),
border_mode='same',
name='pool3')(net['conv3_3'])
# Block 4
net['conv4_1'] = Convolution2D(512,
3,
3,
activation='relu',
W_regularizer=l2(weight_decay),
border_mode='same',
name='conv4_1')(net['pool3'])
net['conv4_2'] = Convolution2D(512,
3,
3,
activation='relu',
W_regularizer=l2(weight_decay),
border_mode='same',
name='conv4_2')(net['conv4_1'])
net['conv4_3'] = Convolution2D(512,
3,
3,
activation='relu',
W_regularizer=l2(weight_decay),
border_mode='same',
name='conv4_3')(net['conv4_2'])
net['pool4'] = MaxPooling2D((2, 2),
strides=(2, 2),
border_mode='same',
name='pool4')(net['conv4_3'])
# Block 5
net['conv5_1'] = Convolution2D(512,
3,
3,
activation='relu',
W_regularizer=l2(weight_decay),
border_mode='same',
name='conv5_1')(net['pool4'])
net['conv5_2'] = Convolution2D(512,
3,
3,
activation='relu',
W_regularizer=l2(weight_decay),
border_mode='same',
name='conv5_2')(net['conv5_1'])
net['conv5_3'] = Convolution2D(512,
3,
3,
activation='relu',
W_regularizer=l2(weight_decay),
border_mode='same',
name='conv5_3')(net['conv5_2'])
net['pool5'] = MaxPooling2D((3, 3),
strides=(1, 1),
border_mode='same',
name='pool5')(net['conv5_3'])
if include_top:
# FC6
net['fc6'] = AtrousConvolution2D(1024,
3,
3,
atrous_rate=(6, 6),
activation='relu',
border_mode='same',
name='fc6')(net['pool5'])
# x = Dropout(0.5, name='drop6')(x)
# FC7
net['fc7'] = Convolution2D(1024,
1,
1,
activation='relu',
W_regularizer=l2(weight_decay),
border_mode='same',
name='fc7')(net['fc6'])
# x = Dropout(0.5, name='drop7')(x)
# Block 6
net['conv6_1'] = Convolution2D(256,
1,
1,
activation='relu',
W_regularizer=l2(weight_decay),
border_mode='same',
name='conv6_1')(net['fc7'])
net['conv6_2'] = Convolution2D(512,
3,
3,
subsample=(2, 2),
activation='relu',
border_mode='same',
name='conv6_2')(net['conv6_1'])
# Block 7
net['conv7_1'] = Convolution2D(128,
1,
1,
activation='relu',
W_regularizer=l2(weight_decay),
border_mode='same',
name='conv7_1')(net['conv6_2'])
net['conv7_2'] = ZeroPadding2D()(net['conv7_1'])
net['conv7_2'] = Convolution2D(256,
3,
3,
subsample=(2, 2),
activation='relu',
W_regularizer=l2(weight_decay),
border_mode='valid',
name='conv7_2')(net['conv7_2'])
# Block 8
net['conv8_1'] = Convolution2D(128,
1,
1,
activation='relu',
W_regularizer=l2(weight_decay),
border_mode='same',
name='conv8_1')(net['conv7_2'])
net['conv8_2'] = Convolution2D(256,
3,
3,
subsample=(2, 2),
activation='relu',
W_regularizer=l2(weight_decay),
border_mode='same',
name='conv8_2')(net['conv8_1'])
# Last Pool
net['pool6'] = GlobalAveragePooling2D(name='pool6')(net['conv8_2'])
# Prediction from conv4_3
net['conv4_3_norm'] = Normalize(20,
name='conv4_3_norm')(net['conv4_3'])
num_priors = 3
x = Convolution2D(num_priors * 4,
3,
3,
border_mode='same',
name='conv4_3_norm_mbox_loc')(net['conv4_3_norm'])
net['conv4_3_norm_mbox_loc'] = x
flatten = Flatten(name='conv4_3_norm_mbox_loc_flat')
net['conv4_3_norm_mbox_loc_flat'] = flatten(
net['conv4_3_norm_mbox_loc'])
name = 'conv4_3_norm_mbox_conf'
if num_classes != 21:
name += '_{}'.format(num_classes)
x = Convolution2D(num_priors * num_classes,
3,
3,
border_mode='same',
name=name)(net['conv4_3_norm'])
net['conv4_3_norm_mbox_conf'] = x
flatten = Flatten(name='conv4_3_norm_mbox_conf_flat')
net['conv4_3_norm_mbox_conf_flat'] = flatten(
net['conv4_3_norm_mbox_conf'])
priorbox = PriorBox(img_size,
30.0,
aspect_ratios=[2],
variances=[0.1, 0.1, 0.2, 0.2],
name='conv4_3_norm_mbox_priorbox')
net['conv4_3_norm_mbox_priorbox'] = priorbox(net['conv4_3_norm'])
# Prediction from fc7
num_priors = 6
net['fc7_mbox_loc'] = Convolution2D(num_priors * 4,
3,
3,
border_mode='same',
name='fc7_mbox_loc')(net['fc7'])
flatten = Flatten(name='fc7_mbox_loc_flat')
net['fc7_mbox_loc_flat'] = flatten(net['fc7_mbox_loc'])
name = 'fc7_mbox_conf'
if num_classes != 21:
name += '_{}'.format(num_classes)
net['fc7_mbox_conf'] = Convolution2D(num_priors * num_classes,
3,
3,
border_mode='same',
name=name)(net['fc7'])
flatten = Flatten(name='fc7_mbox_conf_flat')
net['fc7_mbox_conf_flat'] = flatten(net['fc7_mbox_conf'])
priorbox = PriorBox(img_size,
60.0,
max_size=114.0,
aspect_ratios=[2, 3],
variances=[0.1, 0.1, 0.2, 0.2],
name='fc7_mbox_priorbox')
net['fc7_mbox_priorbox'] = priorbox(net['fc7'])
# Prediction from conv6_2
num_priors = 6
x = Convolution2D(num_priors * 4,
3,
3,
border_mode='same',
name='conv6_2_mbox_loc')(net['conv6_2'])
net['conv6_2_mbox_loc'] = x
flatten = Flatten(name='conv6_2_mbox_loc_flat')
net['conv6_2_mbox_loc_flat'] = flatten(net['conv6_2_mbox_loc'])
name = 'conv6_2_mbox_conf'
if num_classes != 21:
name += '_{}'.format(num_classes)
x = Convolution2D(num_priors * num_classes,
3,
3,
border_mode='same',
name=name)(net['conv6_2'])
net['conv6_2_mbox_conf'] = x
flatten = Flatten(name='conv6_2_mbox_conf_flat')
net['conv6_2_mbox_conf_flat'] = flatten(net['conv6_2_mbox_conf'])
priorbox = PriorBox(img_size,
114.0,
max_size=168.0,
aspect_ratios=[2, 3],
variances=[0.1, 0.1, 0.2, 0.2],
name='conv6_2_mbox_priorbox')
net['conv6_2_mbox_priorbox'] = priorbox(net['conv6_2'])
# Prediction from conv7_2
num_priors = 6
x = Convolution2D(num_priors * 4,
3,
3,
border_mode='same',
name='conv7_2_mbox_loc')(net['conv7_2'])
net['conv7_2_mbox_loc'] = x
flatten = Flatten(name='conv7_2_mbox_loc_flat')
net['conv7_2_mbox_loc_flat'] = flatten(net['conv7_2_mbox_loc'])
name = 'conv7_2_mbox_conf'
if num_classes != 21:
name += '_{}'.format(num_classes)
x = Convolution2D(num_priors * num_classes,
3,
3,
border_mode='same',
name=name)(net['conv7_2'])
net['conv7_2_mbox_conf'] = x
flatten = Flatten(name='conv7_2_mbox_conf_flat')
net['conv7_2_mbox_conf_flat'] = flatten(net['conv7_2_mbox_conf'])
priorbox = PriorBox(img_size,
168.0,
max_size=222.0,
aspect_ratios=[2, 3],
variances=[0.1, 0.1, 0.2, 0.2],
name='conv7_2_mbox_priorbox')
net['conv7_2_mbox_priorbox'] = priorbox(net['conv7_2'])
# Prediction from conv8_2
num_priors = 6
x = Convolution2D(num_priors * 4,
3,
3,
border_mode='same',
name='conv8_2_mbox_loc')(net['conv8_2'])
net['conv8_2_mbox_loc'] = x
flatten = Flatten(name='conv8_2_mbox_loc_flat')
net['conv8_2_mbox_loc_flat'] = flatten(net['conv8_2_mbox_loc'])
name = 'conv8_2_mbox_conf'
if num_classes != 21:
name += '_{}'.format(num_classes)
x = Convolution2D(num_priors * num_classes,
3,
3,
border_mode='same',
name=name)(net['conv8_2'])
net['conv8_2_mbox_conf'] = x
flatten = Flatten(name='conv8_2_mbox_conf_flat')
net['conv8_2_mbox_conf_flat'] = flatten(net['conv8_2_mbox_conf'])
priorbox = PriorBox(img_size,
222.0,
max_size=276.0,
aspect_ratios=[2, 3],
variances=[0.1, 0.1, 0.2, 0.2],
name='conv8_2_mbox_priorbox')
net['conv8_2_mbox_priorbox'] = priorbox(net['conv8_2'])
# Prediction from pool6
num_priors = 6
x = Dense(num_priors * 4, name='pool6_mbox_loc_flat')(net['pool6'])
net['pool6_mbox_loc_flat'] = x
name = 'pool6_mbox_conf_flat'
if num_classes != 21:
name += '_{}'.format(num_classes)
x = Dense(num_priors * num_classes, name=name)(net['pool6'])
net['pool6_mbox_conf_flat'] = x
priorbox = PriorBox(img_size,
276.0,
max_size=330.0,
aspect_ratios=[2, 3],
variances=[0.1, 0.1, 0.2, 0.2],
name='pool6_mbox_priorbox')
if K.image_dim_ordering() == 'tf':
target_shape = (1, 1, 256)
else:
target_shape = (256, 1, 1)
net['pool6_reshaped'] = Reshape(target_shape,
name='pool6_reshaped')(net['pool6'])
net['pool6_mbox_priorbox'] = priorbox(net['pool6_reshaped'])
# Gather all predictions
net['mbox_loc'] = merge([
net['conv4_3_norm_mbox_loc_flat'], net['fc7_mbox_loc_flat'],
net['conv6_2_mbox_loc_flat'], net['conv7_2_mbox_loc_flat'],
net['conv8_2_mbox_loc_flat'], net['pool6_mbox_loc_flat']
],
mode='concat',
concat_axis=1,
name='mbox_loc')
net['mbox_conf'] = merge([
net['conv4_3_norm_mbox_conf_flat'], net['fc7_mbox_conf_flat'],
net['conv6_2_mbox_conf_flat'], net['conv7_2_mbox_conf_flat'],
net['conv8_2_mbox_conf_flat'], net['pool6_mbox_conf_flat']
],
mode='concat',
concat_axis=1,
name='mbox_conf')
net['mbox_priorbox'] = merge([
net['conv4_3_norm_mbox_priorbox'], net['fc7_mbox_priorbox'],
net['conv6_2_mbox_priorbox'], net['conv7_2_mbox_priorbox'],
net['conv8_2_mbox_priorbox'], net['pool6_mbox_priorbox']
],
mode='concat',
concat_axis=1,
name='mbox_priorbox')
if hasattr(net['mbox_loc'], '_keras_shape'):
num_boxes = net['mbox_loc']._keras_shape[-1] // 4
elif hasattr(net['mbox_loc'], 'int_shape'):
num_boxes = K.int_shape(net['mbox_loc'])[-1] // 4
net['mbox_loc'] = Reshape((num_boxes, 4),
name='mbox_loc_final')(net['mbox_loc'])
net['mbox_conf'] = Reshape((num_boxes, num_classes),
name='mbox_conf_logits')(net['mbox_conf'])
net['mbox_conf'] = Activation('softmax',
name='mbox_conf_final')(net['mbox_conf'])
net['predictions'] = merge(
[net['mbox_loc'], net['mbox_conf'], net['mbox_priorbox']],
mode='concat',
concat_axis=2,
name='predictions')
ssd = Model(net['input'], net['predictions'])
else:
ssd = Model(net['input'], net['pool5'])
return ssd, net
#importing keras model and layers to construct LSTM model
from keras.models import Sequential
from keras.layers import Dense, Flatten, LSTM, Dropout
#initializing regression model
regressor = Sequential()
#adding layer(s) to model
regressor.add(LSTM(units=50, return_sequences=True, input_shape=(x_train.shape[1], x_train.shape[2])))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units=50, return_sequences=True ))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units=33, return_sequences=True))
regressor.add(Flatten())
regressor.add(Dense(units=1))
#compiling the model with mean_absolute_percentage_error and adam optimizer
regressor.compile(optimizer='adam', loss='mean_absolute_percentage_error')
#fitting model with training sets and validation set
history = regressor.fit(x_train, y_train, epochs = EPOCH, batch_size=BATCH_SIZE, validation_data=(x_test, y_test))
bm.save_val_loss_plot(history, PLACE+"_epoch_history.csv")
results = regressor.predict(x_test)
#constructing estimation dataframe
real_values = pd.DataFrame(index = test.index,
data = bm.inverse_scale(sc, y_test.reshape(-1, 1)),
columns = ['Real'])
# layer1_model3.add(Convolution2D(layer1, 6,1293,
# border_mode='valid',
# input_shape=(1, 7, 1293)))
# layer1_model3.add(Activation('tanh'))
# layer1_model3.add(MaxPooling2D(pool_size=(nb_pool[0], nb_pool[1])))
model = merge([m1,m2,m3],mode='concat',concat_axis=2)
# model = Sequential()
# model.add(Merge([layer1_model2,layer1_model1,layer1_model3], mode='concat',concat_axis=2))
# model = model([l1_input,l1_input,l1_input])
model=MaxPooling2D(pool_size=(nb_pool[0], nb_pool[1]))(model)
# model.add(Dropout(0.25))
model=Flatten()(model) #平铺
# model.add(Dense(hidden1)) #Full connection 1: 1000
# model.add(Activation('tanh'))
# model.add(Dropout(0.5))
# model.add(Dense(hidden2)) #Full connection 2: 200
# model.add(Activation('tanh'))
# model.add(Dropout(0.5))
model=Dense(nb_classes)(model)
model=Activation('softmax')(model)
# model.summary()
model = Model(input=[l1_input], output=[model])
model.summary()
x = Conv2D(30, (5, 5), padding='same', activation='relu')(x)
print(x.shape)
x = MaxPooling2D((2,2))(x)
print(x.shape)
x = Conv2D(35, (5, 5), padding='same', activation='relu')(x)
print(x.shape)
x = MaxPooling2D((2,2))(x)
print(x.shape)
x = Conv2D(40, (5, 5), padding='same', activation='relu')(x)
print(x.shape)
x = MaxPooling2D((2,2))(x)
print(x.shape)
print('pre flatten shape')
shape = K.int_shape(x)
print(shape)
x = Flatten()(x)
print(x.get_shape())
# time.sleep(20)
# z_mean = Dense(latentDim, name='z_mean')(x)
# z_log_var = Dense(latentDim, name='z_log_var')(x)
# print(z_mean.shape)
# print(z_log_var.shape)
# z = Lambda(sampling, output_shape=(latentDim,), name='z')([z_mean, z_log_var])
encoder_model = Model(encoder_in, x)
#This ensures the model will be shared, including weights
encoded1 = encoder_model(image1)
encoded2 = encoder_model(image2)
