def construct_inceptionv4onfire(x,y, training=False, enable_batch_norm=True): network = input_data(shape=[None, y, x, 3]) #stem of inceptionV4 conv1_3_3 = conv_2d(network,32,3,strides=2,activation='relu',name='conv1_3_3_s2',padding='valid') conv2_3_3 = conv_2d(conv1_3_3,32,3,activation='relu',name='conv2_3_3') conv3_3_3 = conv_2d(conv2_3_3,64,3,activation='relu',name='conv3_3_3') b_conv_1_pool = max_pool_2d(conv3_3_3,kernel_size=3,strides=2,padding='valid',name='b_conv_1_pool') if enable_batch_norm: b_conv_1_pool = batch_normalization(b_conv_1_pool) b_conv_1_conv = conv_2d(conv3_3_3,96,3,strides=2,padding='valid',activation='relu',name='b_conv_1_conv') b_conv_1 = merge([b_conv_1_conv,b_conv_1_pool],mode='concat',axis=3) b_conv4_1_1 = conv_2d(b_conv_1,64,1,activation='relu',name='conv4_3_3') b_conv4_3_3 = conv_2d(b_conv4_1_1,96,3,padding='valid',activation='relu',name='conv5_3_3') b_conv4_1_1_reduce = conv_2d(b_conv_1,64,1,activation='relu',name='b_conv4_1_1_reduce') b_conv4_1_7 = conv_2d(b_conv4_1_1_reduce,64,[1,7],activation='relu',name='b_conv4_1_7') b_conv4_7_1 = conv_2d(b_conv4_1_7,64,[7,1],activation='relu',name='b_conv4_7_1') b_conv4_3_3_v = conv_2d(b_conv4_7_1,96,3,padding='valid',name='b_conv4_3_3_v') b_conv_4 = merge([b_conv4_3_3_v, b_conv4_3_3],mode='concat',axis=3) b_conv5_3_3 = conv_2d(b_conv_4,192,3,padding='valid',activation='relu',name='b_conv5_3_3',strides=2) b_pool5_3_3 = max_pool_2d(b_conv_4,kernel_size=3,padding='valid',strides=2,name='b_pool5_3_3') if enable_batch_norm: b_pool5_3_3 = batch_normalization(b_pool5_3_3) b_conv_5 = merge([b_conv5_3_3,b_pool5_3_3],mode='concat',axis=3) net = b_conv_5 # inceptionV4 modules net=inception_block_a(net) net=inception_block_b(net) net=inception_block_c(net) pool5_7_7=global_avg_pool(net) if(training): pool5_7_7=dropout(pool5_7_7,0.4) loss = fully_connected(pool5_7_7, 2,activation='softmax') if(training): network = regression(loss, optimizer='rmsprop', loss='categorical_crossentropy', learning_rate=0.001) else: network=loss model = tflearn.DNN(network, checkpoint_path='inceptionv4onfire', max_checkpoints=1, tensorboard_verbose=0) return model
def _bottleneck(input_net, filters, kernel, t, s, r=False): """Bottleneck This function defines a basic bottleneck structure. Parameters ---------- input_net: Tensor, input tensor of conv layer. filters: Integer, the dimensionality of the output space. kernel: An integer or tuple/list of 2 integers, specifying the width and height of the 2D convolution window. t: Integer, expansion factor. t is always applied to the input size. s: An integer or tuple/list of 2 integers,specifying the strides of the convolution along the width and height.Can be a single integer to specify the same value for all spatial dimensions. r: Boolean, Whether to use the residuals. # Returns Output tensor. """ t_channel = input_net.shape[3] * t # channel expansion net = _conv_block(input_net, t_channel, (1, 1), (1, 1)) net = grouped_conv_2d(net, channel_multiplier=1, filter_size=kernel, strides=(s, s), padding='same', activation='relu6', weights_init='xavier') net = batch_normalization(net) net = conv_2d(net, filters, (1, 1), strides=(1, 1), padding='same') net = batch_normalization(net) if r: net = merge([net, input_net], 'elemwise_sum') return net
def cnn(self): network = input_data(shape=[None, self.max_document_length], name='input') network = tflearn.embedding(network, input_dim=1000000, output_dim=128) branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2") branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2") branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2") network = merge([branch1, branch2, branch3], mode='concat', axis=1) network = tf.expand_dims(network, 2) network = global_max_pool(network) network = dropout(network, 0.8) network = fully_connected(network, 2, activation='softmax') network = regression(network, optimizer='adam', learning_rate=0.001, loss='categorical_crossentropy', name='target') model = tflearn.DNN(network, tensorboard_verbose=0) return model
def transform_embedded_sequences(self, embedded_sequences): drop_1, drop_2 = self.dropout_rates net = dropout(embedded_sequences, drop_1) conv_blocks = [] for sz in self.filter_sizes: conv = conv_1d(net, nb_filter=self.num_filters, filter_size=sz, padding="valid", activation="relu", regularizer="L2") conv_blocks.append(conv) net = merge(conv_blocks, mode='concat', axis=1) if len(conv_blocks) > 1 else conv_blocks[0] net = tf.expand_dims(net, 2) net = global_max_pool(net) net = dropout(net, drop_2) model_output = fully_connected(net, self.class_count, activation="softmax") return model_output
def conv_model(network): branch1 = conv_1d(network, 200, 3, padding='valid', activation='relu', regularizer="L2") branch2 = conv_1d(network, 200, 4, padding='valid', activation='relu', regularizer="L2") branch3 = conv_1d(network, 200, 5, padding='valid', activation='relu', regularizer="L2") network = merge([branch1, branch2, branch3], mode='concat', axis=1) network = tf.expand_dims(network, 2) network = global_max_pool(network) network = dropout(network, 0.5) return network
def get_network(): # input images network = input_data(shape=[None,33,33,4], name='input') # local pathway [conv + pool + norm] #path1 = conv_2d(network, 32, 7, activation='relu', regularizer="L2", padding='valid') path1 = conv_2d(network, 64, 7, activation='relu', regularizer="L2", padding='valid') path1 = max_pool_2d(path1, 4, 1, padding='valid') path1 = dropout(path1, 0.5) #path1 = local_response_normalization(path1) #path1 = conv_2d(path1, 32, 3, activation='relu', regularizer="L2", padding='valid') path1 = conv_2d(path1, 64, 3, activation='relu', regularizer="L2", padding='valid') path1 = max_pool_2d(path1, 2, 1, padding='valid') path1 = dropout(path1, 0.5) #path1 = local_response_normalization(path1) # global pathway #path2 = conv_2d(network, 80, 13, activation='relu', regularizer="L2", padding='valid') path2 = conv_2d(network, 160, 13, activation='relu', regularizer="L2", padding='valid') path2 = dropout(path2, 0.5) #path2 = local_response_normalization(path2) network = merge([path1,path2],'concat',axis=3) network = conv_2d(network, 5, 21, activation='relu', regularizer="L2") network = flatten(network, name="flatten") # softmax + output layers network = fully_connected(network, 5, activation='softmax', name='soft') network = regression(network, optimizer='adam', learning_rate=0.00005, # 0.0001 loss='categorical_crossentropy', name='target', batch_size=500) return network
def get_cnn_model(max_len, volcab_size): # Building convolutional network network = tflearn.input_data(shape=[None, max_len], name='input') network = tflearn.embedding(network, input_dim=volcab_size, output_dim=64) branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2") branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2") branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2") network = merge([branch1, branch2, branch3], mode='concat', axis=1) network = tf.expand_dims(network, 2) network = global_max_pool(network) network = dropout(network, 0.5) network = fully_connected(network, 3, activation='softmax') network = regression(network, optimizer='adam', learning_rate=0.001, loss='categorical_crossentropy', name='target') model = tflearn.DNN(network, tensorboard_verbose=0) return model
def conv_reflectance_neural_network_model5( n_actions=3, name="conv_ref4", shape=None, imconstpar=constantParametersImage(), netconstpar=constantParametersNetwork()): convnet = input_data( shape=[None, imconstpar.height, imconstpar.width, imconstpar.channels], name='image_input') reflectance = tflearn.input_data(shape=[None, 6], name="reflectance_input") convnet = conv_2d(convnet, 32, 8, strides=4, activation='prelu', name='conv1') #convnet = max_pool_2d(convnet, 2) convnet = conv_2d(convnet, 64, 4, strides=2, activation='prelu', name='conv2') #convnet = max_pool_2d(convnet, 2) convnet = conv_2d(convnet, 64, 3, strides=1, activation='prelu', name='conv3') #convnet = max_pool_2d(convnet, 2) convnet = fully_connected(convnet, 128, activation='softmax', name='fc1') convnet = dropout(convnet, .8) convnet = fully_connected(convnet, 3, activation='sigmoid', name='fc2') merged_net = merge([convnet, reflectance], mode='concat', axis=1) #convnet = regression(convnet, optimizer='SGD', loss='categorical_crossentropy', name='targets') merged_net = fully_connected(merged_net, 12, activation='softmax', name='m_fc1') merged_net = fully_connected(merged_net, n_actions, activation='linear', name='m_fc2') merged_net = regression(merged_net, optimizer='adam', learning_rate=netconstpar.l_rate, loss='mean_square', name='targets') model = tflearn.DNN(merged_net) return model, name
def model_M_bn_cnn_1_1_filter(Input, tt_ll, training): branch0 = M_cnn(Input=Input, num=53, training=training, tt_ll=tt_ll) branch1 = M_cnn(Input=Input, num=43, training=training, tt_ll=tt_ll) branch2 = M_cnn(Input=Input, num=33, training=training, tt_ll=tt_ll) branch3 = M_cnn(Input=Input, num=23, training=training, tt_ll=tt_ll) branch4 = M_cnn(Input=Input, num=13, training=training, tt_ll=tt_ll) branch5 = M_cnn(Input=Input, num=11, training=training, tt_ll=tt_ll) branch6 = M_cnn(Input=Input, num=9, training=training, tt_ll=tt_ll) branch7 = M_cnn(Input=Input, num=7, training=training, tt_ll=tt_ll) branch8 = M_cnn(Input=Input, num=5, training=training, tt_ll=tt_ll) branch9 = M_cnn(Input=Input, num=3, training=training, tt_ll=tt_ll) #TensorShape([Dimension(None), Dimension(300), Dimension(32)]) network_out = merge([ branch0, branch1, branch2, branch3, branch4, branch5, branch6, branch7, branch8, branch9 ], mode='concat', axis=2) #TensorShape([Dimension(None), Dimension(300), Dimension(320)]) #network_feature_1 = fully_connected(network_out, 1280, activation='relu') #network_feature = fully_connected(network_feature_1, 128, activation='relu') network_feature = dropout(network_out, 0.9) Output = fully_connected(network_feature, 2, activation='relu') return Output
def do_cnn(trainX, trainY,testX, testY): global n_words # Data preprocessing # Sequence padding trainX = pad_sequences(trainX, maxlen=MAX_DOCUMENT_LENGTH, value=0.) testX = pad_sequences(testX, maxlen=MAX_DOCUMENT_LENGTH, value=0.) # Converting labels to binary vectors trainY = to_categorical(trainY, nb_classes=2) testY = to_categorical(testY, nb_classes=2) # Building convolutional network network = input_data(shape=[None, MAX_DOCUMENT_LENGTH], name='input') network = tflearn.embedding(network, input_dim=n_words+1, output_dim=128) branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2") branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2") branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2") network = merge([branch1, branch2, branch3], mode='concat', axis=1) network = tf.expand_dims(network, 2) network = global_max_pool(network) network = dropout(network, 0.5) network = fully_connected(network, 2, activation='softmax') network = regression(network, optimizer='adam', learning_rate=0.001, loss='categorical_crossentropy', name='target') # Training model = tflearn.DNN(network, tensorboard_verbose=0) model.fit(trainX, trainY, n_epoch = 20, shuffle=True, validation_set=(testX, testY), show_metric=True, batch_size=32)
def do_cnn_word2vec(trainX, testX, trainY, testY): global max_features print "CNN and word2vec" #trainX = pad_sequences(trainX, maxlen=max_document_length, value=-1.) #testX = pad_sequences(testX, maxlen=max_document_length, value=0.) # Converting labels to binary vectors trainY = to_categorical(trainY, nb_classes=2) testY = to_categorical(testY, nb_classes=2) # Building convolutional network network = input_data(shape=[None,max_features], name='input') network = tflearn.embedding(network, input_dim=1000000, output_dim=128) branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2") branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2") branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2") network = merge([branch1, branch2, branch3], mode='concat', axis=1) network = tf.expand_dims(network, 2) network = global_max_pool(network) network = dropout(network, 0.8) network = fully_connected(network, 2, activation='softmax') network = regression(network, optimizer='adam', learning_rate=0.001, loss='categorical_crossentropy', name='target') # Training model = tflearn.DNN(network, tensorboard_verbose=0) model.fit(trainX, trainY, n_epoch=5, shuffle=True, validation_set=(testX, testY), show_metric=True, batch_size=2,run_id="spam")
def build(): network = input_data([None, Meta.max_string_len]) network = embedding(network, input_dim=Meta.max_one_hot, output_dim=128) branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2") branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2") branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2") network = merge([branch1, branch2, branch3], mode='concat', axis=1) network = dropout(network, 0.5) network = lstm(network, 128) # network = fully_connected(network, 20) network = fully_connected(network, 2, activation='softmax') network = tflearn.regression(network, optimizer='adam', learning_rate=0.001, loss='categorical_crossentropy') model = tflearn.DNN(network, tensorboard_verbose=0) return model
def build_network(optimizer): net = input_data(shape=[None, length], name='input') net = tflearn.embedding(net, input_dim=caes_ngram_data.dims, output_dim=128) branch1 = conv_1d(net, 128, 3, padding='valid', activation='relu', regularizer="L2") branch2 = conv_1d(net, 128, 4, padding='valid', activation='relu', regularizer="L2") branch3 = conv_1d(net, 128, 5, padding='valid', activation='relu', regularizer="L2") net = merge([branch1, branch2, branch3], mode='concat', axis=1) net = tf.expand_dims(net, 2) net = global_max_pool(net) net = dropout(net, 0.33) net = fully_connected(net, 6, activation='softmax') net = regression(net, optimizer=optimizer, learning_rate=0.001, loss='categorical_crossentropy', name='target') return net
def do_cnn_doc2vec(trainX, testX, trainY, testY): global max_features print "CNN and doc2vec" #trainX = pad_sequences(trainX, maxlen=max_features, value=0.) #testX = pad_sequences(testX, maxlen=max_features, value=0.) # Converting labels to binary vectors trainY = to_categorical(trainY, nb_classes=2) testY = to_categorical(testY, nb_classes=2) # Building convolutional network network = input_data(shape=[None,max_features], name='input') network = tflearn.embedding(network, input_dim=1000000, output_dim=128,validate_indices=False) branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2") branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2") branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2") network = merge([branch1, branch2, branch3], mode='concat', axis=1) network = tf.expand_dims(network, 2) network = global_max_pool(network) network = dropout(network, 0.8) network = fully_connected(network, 2, activation='softmax') network = regression(network, optimizer='adam', learning_rate=0.001, loss='categorical_crossentropy', name='target') # Training model = tflearn.DNN(network, tensorboard_verbose=0) model.fit(trainX, trainY, n_epoch=5, shuffle=True, validation_set=(testX, testY), show_metric=True, batch_size=100,run_id="review")
def cnn_3_filters(trainX, trainY, valX, valY, testX, input_weights): ''' A CNN with three convolutional layers as in Kim Yoon (Convolutional Neural Networks for Sentence Classification) ''' # Building convolutional network network = input_data(shape=[None, MAX_LENGHT], name='input') network = tflearn.embedding(network, input_dim=input_weights.shape[0], output_dim=input_weights.shape[1], trainable=True, name="EmbeddingLayer") branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2") branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2") branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2") network = merge([branch1, branch2, branch3], mode='concat', axis=1) network = tf.expand_dims(network, 2) network = global_max_pool(network) network = dropout(network, 0.5) network = fully_connected(network, 12, activation='softmax') network = regression(network, optimizer='adam', learning_rate=0.001, loss='categorical_crossentropy', name='target') # Training model = tflearn.DNN(network, tensorboard_verbose=1) # Add embedding weights into the embedding layer embeddingWeights = tflearn.get_layer_variables_by_name("EmbeddingLayer")[0] model.set_weights(embeddingWeights, input_weights) print("Start trianing CNN...") model.fit(trainX, trainY, n_epoch=NB_EPOCHS, validation_set=(valX, valY), shuffle=True, show_metric=True, batch_size=32) y_result = model.predict(testX) return y_result
def get_network(): network = input_data(shape=[None, LEN_HISTORY], name="inp") network = reshape(network, [tf.shape(network)[0], LEN_HISTORY, 1], name="added_inp_dim") network = lstm(network, LEN_HISTORY, name="hidden", return_seq=True) network = [fully_connected(ts, 1, activation='softmax') for ts in network] network = merge(network, "concat", axis=1, name="concat_merge") network = network / tf.expand_dims(tf.reduce_mean(network, axis=1), 1, name="normed") network = regression(network, optimizer="sgd", loss=revenue) return network
def inception(self, network, filter_num, activation='relu', regularizer=None, strides=1, trainable=True): network1 = conv_2d(network, int(filter_num / 3), 1, activation='relu', strides=1, regularizer=regularizer, trainable=trainable) network1 = conv_2d(network1, filter_num, 5, activation='relu', strides=strides, regularizer=regularizer, trainable=trainable) network2 = conv_2d(network, int(filter_num / 3), 1, activation='relu', strides=1, regularizer=regularizer, trainable=trainable) network2 = conv_2d(network2, filter_num, 3, activation='relu', strides=strides, regularizer=regularizer, trainable=trainable) network3 = conv_2d(network, int(filter_num / 3), 1, activation='relu', strides=1, regularizer=regularizer, trainable=trainable) network3 = conv_2d(network3, filter_num, 2, activation='relu', strides=strides, regularizer=regularizer, trainable=trainable) # network4 = max_pool_2d(network, kernel_size=2, strides=strides) # network5 = max_pool_2d(network, kernel_size=3, strides=strides) # network6 = max_pool_2d(network, kernel_size=5, strides=strides) network = merge([network1, network2, network3], mode='concat', axis=3) return network
def inception_block_a(input_a): inception_a_conv1_1_1 = conv_2d(input_a, 96, 1, activation='relu', name='inception_a_conv1_1_1') inception_a_conv1_3_3_reduce = conv_2d(input_a, 64, 1, activation='relu', name='inception_a_conv1_3_3_reduce') inception_a_conv1_3_3 = conv_2d(inception_a_conv1_3_3_reduce, 96, 3, activation='relu', name='inception_a_conv1_3_3') inception_a_conv2_3_3_reduce = conv_2d(input_a, 64, 1, activation='relu', name='inception_a_conv2_3_3_reduce') inception_a_conv2_3_3_sym_1 = conv_2d(inception_a_conv2_3_3_reduce, 96, 3, activation='relu', name='inception_a_conv2_3_3') inception_a_conv2_3_3 = conv_2d(inception_a_conv2_3_3_sym_1, 96, 3, activation='relu', name='inception_a_conv2_3_3') inception_a_pool = avg_pool_2d(input_a, kernel_size=3, name='inception_a_pool', strides=1) inception_a_pool_1_1 = conv_2d(inception_a_pool, 96, 1, activation='relu', name='inception_a_pool_1_1') # merge inception_a inception_a = merge([ inception_a_conv1_1_1, inception_a_conv1_3_3, inception_a_conv2_3_3, inception_a_pool_1_1 ], mode='concat', axis=3) return inception_a
def create_model_for_multiple_input(self): # main cnn model network1 = input_data( shape=[None, self.title_mat_size, self.title_mat_size, 1]) network2 = input_data( shape=[None, self.author_mat_size, self.author_mat_size, 1]) activation = 'relu' # activation = 'tanh' # activation = 'sigmoid' network1 = conv_2d(network1, 8, 3, activation=activation, regularizer="L2") # network = max_pool_2d(network, 2) # network = local_response_normalization(network) network1 = conv_2d(network1, 16, 2, activation=activation, regularizer="L2") # network = max_pool_2d(network, 2) # network = local_response_normalization(network) network1 = fully_connected(network1, 512, activation=activation) network2 = conv_2d(network2, 8, 2, activation=activation, regularizer='L2') network2 = fully_connected(network2, 512, activation=activation) network = merge([network1, network2], 'concat') network = dropout(network, 0.5) network = fully_connected(network, 2, activation='softmax') acc = Accuracy(name="Accuracy") adagrad = tflearn.AdaGrad(learning_rate=0.002, initial_accumulator_value=0.01) network = regression(network, optimizer=adagrad, loss='categorical_crossentropy', learning_rate=0.002, metric=acc) tmp_dir = join(settings.TRAIN_DIR, 'tmp') # util.mkdir(tmp_dir) model = tflearn.DNN(network, checkpoint_path=join(tmp_dir, 'paper_similarity'), max_checkpoints=10, tensorboard_verbose=3, tensorboard_dir=join(tmp_dir, 'tflearn_logs')) return model
def do_cnn(x,y): print("start CNN......") global max_document_length print("CNN and tf") trainX, testX, trainY, testY = train_test_split(x, y, test_size=0.4, random_state=0) y_test=testY # 训练、测试数据进行填充和转换,不到最大长度的数据填充0 trainX = pad_sequences(trainX, maxlen=max_document_length, value=0.) testX = pad_sequences(testX, maxlen=max_document_length, value=0.) # Converting labels to binary vectors # 二分类问题,把标记数据二值化 trainY = to_categorical(trainY, nb_classes=2) testY = to_categorical(testY, nb_classes=2) # Building convolutional network network = input_data(shape=[None,max_document_length], name='input') # 三个数量为128,长度分别为3,4,5的一维卷积函数处理数据 network = tflearn.embedding(network, input_dim=1000000, output_dim=128) branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2") branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2") branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2") network = merge([branch1, branch2, branch3], mode='concat', axis=1) network = tf.expand_dims(network, 2) network = global_max_pool(network) network = dropout(network, 0.8) network = fully_connected(network, 2, activation='softmax') network = regression(network, optimizer='adam', learning_rate=0.001, loss='categorical_crossentropy', name='target') # 实例化CNN对象并进行训练数据,训练5轮 model = tflearn.DNN(network, tensorboard_verbose=0) if not os.path.exists(pkl_file): model.fit(trainX, trainY, n_epoch=5, shuffle=True, validation_set=0.1, show_metric=True, batch_size=100,run_id="webshell") model.save(pkl_file) else: model.load(pkl_file) y_predict_list=model.predict(testX) y_predict=[] for i in y_predict_list: print(i[0]) if i[0] > 0.5: y_predict.append(0) else: y_predict.append(1) print('y_predict_list:') print(y_predict_list) print('y_predict:') print(y_predict) #print y_test do_metrics(y_test, y_predict)
def add_merge_layer(self, prev_layer_names, merge_mode, exclude_from_path=True, **kwargs): prev = [self._layers[name]['layer'] for name in prev_layer_names] layer = { "layer": merge.merge(prev, merge_mode, **kwargs), "type": "Merge", "categories": ["hidden"] } return self._add_layer(layer, exclude_from_path)
def do_cnn(x,y): global max_document_length print "CNN and tf" trainX, testX, trainY, testY = train_test_split(x, y, test_size=0.4, random_state=0) y_test=testY trainX = pad_sequences(trainX, maxlen=max_document_length, value=0.) testX = pad_sequences(testX, maxlen=max_document_length, value=0.) # Converting labels to binary vectors trainY = to_categorical(trainY, nb_classes=2) testY = to_categorical(testY, nb_classes=2) # Building convolutional network network = input_data(shape=[None,max_document_length], name='input') network = tflearn.embedding(network, input_dim=1000000, output_dim=128) branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2") branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2") branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2") network = merge([branch1, branch2, branch3], mode='concat', axis=1) network = tf.expand_dims(network, 2) network = global_max_pool(network) network = dropout(network, 0.8) network = fully_connected(network, 2, activation='softmax') network = regression(network, optimizer='adam', learning_rate=0.001, loss='categorical_crossentropy', name='target') model = tflearn.DNN(network, tensorboard_verbose=0) #if not os.path.exists(pkl_file): # Training model.fit(trainX, trainY, n_epoch=5, shuffle=True, validation_set=0.1, show_metric=True, batch_size=100,run_id="webshell") # model.save(pkl_file) #else: # model.load(pkl_file) y_predict_list=model.predict(testX) #y_predict = list(model.predict(testX,as_iterable=True)) y_predict=[] for i in y_predict_list: print i[0] if i[0] > 0.5: y_predict.append(0) else: y_predict.append(1) print 'y_predict_list:' print y_predict_list print 'y_predict:' print y_predict #print y_test do_metrics(y_test, y_predict)
def create_cnn_architecture_two_layers( self, model_name, outputDim=300, number_of_filters=60, filterSize=[3, 4], padding='same', activation_function_convLayer='relu', regularizer='L2', dropouts=0.5, activation_function_fc='softmax', optimizer='adam', learning_rate=0.001, loss_function='categorical_crossentropy'): if len(filterSize) == 0: filterSize = [3, 4] """ Define input shape and create word embedding """ self.cnn_model = input_data(shape=[None, self.max_words], name='input') self.cnn_model = tflearn.embedding( self.cnn_model, input_dim=len(self.vocabProcessor.vocabulary_), output_dim=outputDim) """ Add three/two convolutional layer. Set number of filters and filter sizes and then merge together """ conv1 = conv_1d(self.cnn_model, nb_filter=number_of_filters, filter_size=filterSize[0], padding=padding, activation=activation_function_convLayer, regularizer=regularizer) conv2 = conv_1d(self.cnn_model, nb_filter=number_of_filters, filter_size=filterSize[1], padding=padding, activation=activation_function_convLayer, regularizer=regularizer) #conv3 = conv_1d(cnn_model, nb_filter = 128, filter_size = 5, padding = 'same', # activation = 'relu', regularizer = 'L2') self.cnn_model = merge([conv1, conv2], mode='concat', axis=1) """ Expand one dimension to fit the max_pooling layer """ self.cnn_model = tf.expand_dims(self.cnn_model, 1) self.cnn_model = global_max_pool(self.cnn_model) """ Instantiate dropout layer and specify dropout parameter """ self.cnn_model = dropout(self.cnn_model, dropouts) """ Instantiate fully connected layer and regression layer. """ self.cnn_model = fully_connected(self.cnn_model, self.number_of_classes, activation=activation_function_fc) self.cnn_model = regression(self.cnn_model, optimizer=optimizer, learning_rate=learning_rate, loss=loss_function, name='models/' + model_name)
def block17(net, scale=1.0, activation="relu"): tower_conv = relu( batch_normalization( conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_1x1'))) tower_conv_1_0 = relu( batch_normalization( conv_2d(net, 128, 1, bias=False, activation=None, name='Conv2d_0a_1x1'))) tower_conv_1_1 = relu( batch_normalization( conv_2d(tower_conv_1_0, 160, [1, 7], bias=False, activation=None, name='Conv2d_0b_1x7'))) tower_conv_1_2 = relu( batch_normalization( conv_2d(tower_conv_1_1, 192, [7, 1], bias=False, activation=None, name='Conv2d_0c_7x1'))) tower_mixed = merge([tower_conv, tower_conv_1_2], mode='concat', axis=3) tower_out = relu( batch_normalization( conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1'))) net += scale * tower_out if activation: if isinstance(activation, str): net = activations.get(activation)(net) elif hasattr(activation, '__call__'): net = activation(net) else: raise ValueError("Invalid Activation.") return net
def build_fcn_all(network): #Pool1 conv1 = conv_2d(network, 8, 7, activation='relu') pool1 = max_pool_2d(conv1, 2) #Pool2 conv2 = conv_2d(pool1, 16, 5, activation='relu') pool2 = max_pool_2d(conv2, 2) #Pool3 conv3 = conv_2d(pool2, 32, 5, activation='relu') pool3 = max_pool_2d(conv3, 2) # output 8x_downsampled #Pool4 conv4 = conv_2d(pool3, 64, 3, activation='relu') pool4 = max_pool_2d(conv4, 2) # output 16x_downsampled #start FCN-32s ----------------------------------- #Pool5 conv5 = conv_2d(pool4, 128, 3, activation='relu') pool5 = max_pool_2d(conv5, 2) #Conv6-7 conv6 = conv_2d(pool5, 128, 3, activation='relu') conv7 = conv_2d(conv6, 128, 3, activation='relu') # output 32x_downsampled #end FCN-32s ----------------------------------- ##start FCN-16s ----------------------------------- #network_32_UP2 = upsample_2d(network_32, 2) #network_16 = merge([network_32_UP2, network_4], mode='concat', axis=3) #network_16 = conv_2d(network_16, 3, 128, activation='relu') # output 16x_downsampled ##end FCN-16s ----------------------------------- ##start FCN-8s ----------------------------------- #network_32_UP4 = upsample_2d(network_32, 4) #network_16_UP2 = upsample_2d(network_16, 2) #network_3_UP8 = upsample_2d(network_3, 8) pool4_x2 = upsample_2d(pool4, 2) conv7_x4 = upsample_2d(conv7, 4) #network_8 = merge([network_32_UP4, network_4_UP2, network_3], mode='concat', axis=3) fcn_8s = merge([pool3, pool4_x2, conv7_x4], mode='concat', axis=3) fcn_8s = conv_2d(fcn_8s, 3, 1, activation='relu') ##end FCN-8s ----------------------------------- out = conv_2d(fcn_8s, CHANNELS, 1, activation='relu') out = upsample_2d(out, 8) #network_8 = upscore_layer(network_8, num_classes=3, kernel_size=2, strides=8, shape=[384, 1216, 3]) network = tflearn.regression( out, loss='mean_square', #loss='weak_cross_entropy_2d', ) return network
def buildNet(self): #necessary to allow the model to be loaded after it has been trained tf.reset_default_graph() #build input layer to accept 140 chars network = input_data(shape=[None, 72], name='input') #embedded layer network = tflearn.embedding(network, input_dim=len(self.vp.vocabulary_) + 2, output_dim=128) #create three convolutional layers branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2") branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2") branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2") #merge all incoming tensors into a single tenso network = merge([branch1, branch2, branch3], mode='concat', axis=1) #expand dimensions of network to 3d tensor, as input is a 1d tensor network = tf.expand_dims(network, 2) #perform reduction operation over input tensor network = global_max_pool(network) #prevent overfitting by including dropout network = dropout(network, 0.8) #output layer network = fully_connected(network, 8, activation='softmax') network = regression(network, optimizer='adam', learning_rate=0.0001, loss='categorical_crossentropy', name='target') return network
def do_cnn_1d(trainX, testX, trainY, testY): # Converting labels to binary vectors trainY = to_categorical(trainY, nb_classes=4) testY = to_categorical(testY, nb_classes=4) # Building convolutional network network = input_data(shape=[None, 1000], name='input') network = tflearn.embedding(network, input_dim=1000000, output_dim=128, validate_indices=False) branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2") branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2") branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2") network = merge([branch1, branch2, branch3], mode='concat', axis=1) network = tf.expand_dims(network, 2) network = global_max_pool(network) network = dropout(network, 0.8) network = fully_connected(network, 4, activation='softmax') network = regression(network, optimizer='adam', learning_rate=0.001, loss='categorical_crossentropy', name='target') # Training model = tflearn.DNN(network, tensorboard_verbose=0) model.fit(trainX, trainY, n_epoch=5, shuffle=True, validation_set=(testX, testY), show_metric=True, batch_size=100, run_id="malware")
def reduction_block_b(reduction_input_b): reduction_b_1_1 = conv_2d(reduction_input_b, 192, 1, activation='relu', name='reduction_b_1_1') reduction_b_1_3 = conv_2d(reduction_b_1_1, 192, 3, strides=2, padding='valid', name='reduction_b_1_3') reduction_b_3_3_reduce = conv_2d(reduction_input_b, 256, filter_size=1, activation='relu', name='reduction_b_3_3_reduce') reduction_b_3_3_asym_1 = conv_2d(reduction_b_3_3_reduce, 256, filter_size=[1, 7], activation='relu', name='reduction_b_3_3_asym_1') reduction_b_3_3_asym_2 = conv_2d(reduction_b_3_3_asym_1, 320, filter_size=[7, 1], activation='relu', name='reduction_b_3_3_asym_2') reduction_b_3_3 = conv_2d(reduction_b_3_3_asym_2, 320, 3, strides=2, activation='relu', padding='valid', name='reduction_b_3_3') reduction_b_pool = max_pool_2d(reduction_input_b, kernel_size=3, strides=2, padding='valid') # merge the reduction_b reduction_b_output = merge( [reduction_b_1_3, reduction_b_3_3, reduction_b_pool], mode='concat', axis=3) return reduction_b_output
def model_2(train_x, train_y, test_x, test_y, embedding_size): # train_x = pad_sequences(train_x, maxlen=100, value=0.) # test_x = pad_sequences(test_x, maxlen=100, value=0.) out_dim = embedding_size # embedding size num_cat = len(train_y[0]) network = input_data(shape=[None, len(train_x[0])], name='input') network = tflearn.embedding(network, input_dim=len(train_x[0]), output_dim=out_dim) # input_dim - vocab size branch1 = conv_1d(network, out_dim, 3, padding='same', activation='relu', regularizer="L2") branch2 = conv_1d(network, out_dim, 4, padding='same', activation='relu', regularizer="L2") branch3 = conv_1d(network, out_dim, 5, padding='same', activation='relu', regularizer="L2") network = merge([branch1, branch2, branch3], mode='concat', axis=1) network = tf.expand_dims(network, 2) network = global_max_pool(network) network = dropout(network, 0.5) network = fully_connected(network, num_cat, activation='softmax') network = regression(network, optimizer='adam', learning_rate=0.001, loss='categorical_crossentropy', name='target') # Training model = tflearn.DNN(network, tensorboard_verbose=0) model.fit(train_x, train_y, n_epoch=5, shuffle=True, validation_set=(test_x, test_y), show_metric=True, batch_size=32) return model
def reduction_block_a(reduction_input_a): reduction_a_conv1_1_1 = conv_2d(reduction_input_a,384,3,strides=2,padding='valid',activation='relu',name='reduction_a_conv1_1_1') reduction_a_conv2_1_1 = conv_2d(reduction_input_a,192,1,activation='relu',name='reduction_a_conv2_1_1') reduction_a_conv2_3_3 = conv_2d(reduction_a_conv2_1_1,224,3,activation='relu',name='reduction_a_conv2_3_3') reduction_a_conv2_3_3_s2 = conv_2d(reduction_a_conv2_3_3,256,3,strides=2,padding='valid',activation='relu',name='reduction_a_conv2_3_3_s2') reduction_a_pool = max_pool_2d(reduction_input_a,strides=2,padding='valid',kernel_size=3,name='reduction_a_pool') # merge reduction_a reduction_a = merge([reduction_a_conv1_1_1,reduction_a_conv2_3_3_s2,reduction_a_pool],mode='concat',axis=3) return reduction_a
def inception_block_c(input_c): inception_c_1_1 = conv_2d(input_c, 256, 1, activation='relu', name='inception_c_1_1') inception_c_3_3_reduce = conv_2d(input_c, 384, filter_size=1, activation='relu', name='inception_c_3_3_reduce') inception_c_3_3_asym_1 = conv_2d(inception_c_3_3_reduce, 256, filter_size=[1,3], activation='relu',name='inception_c_3_3_asym_1') inception_c_3_3_asym_2 = conv_2d(inception_c_3_3_reduce, 256, filter_size=[3,1], activation='relu',name='inception_c_3_3_asym_2') inception_c_3_3=merge([inception_c_3_3_asym_1,inception_c_3_3_asym_2],mode='concat',axis=3) inception_c_5_5_reduce = conv_2d(input_c, 384, filter_size=1, activation='relu', name = 'inception_c_5_5_reduce') inception_c_5_5_asym_1 = conv_2d(inception_c_5_5_reduce, 448, filter_size=[1,3], name = 'inception_c_5_5_asym_1') inception_c_5_5_asym_2 = conv_2d(inception_c_5_5_asym_1, 512, filter_size=[3,1], activation='relu',name='inception_c_5_5_asym_2') inception_c_5_5_asym_3 = conv_2d(inception_c_5_5_asym_2, 256, filter_size=[1,3], activation='relu',name='inception_c_5_5_asym_3') inception_c_5_5_asym_4 = conv_2d(inception_c_5_5_asym_2, 256, filter_size=[3,1], activation='relu',name='inception_c_5_5_asym_4') inception_c_5_5=merge([inception_c_5_5_asym_4,inception_c_5_5_asym_3],mode='concat',axis=3) inception_c_pool = avg_pool_2d(input_c, kernel_size=3, strides=1 ) inception_c_pool_1_1 = conv_2d(inception_c_pool, 256, filter_size=1, activation='relu', name='inception_c_pool_1_1') # merge the inception_c inception_c_output = merge([inception_c_1_1, inception_c_3_3, inception_c_5_5, inception_c_pool_1_1], mode='concat', axis=3) return inception_c_output
def nlp_cnn(trainX, trainY, testX, testY): # pad the sequence trainX = pad_sequences(trainX, maxlen=100, value=0.) testX = pad_sequences(testX, maxlen=100, value=0.) # one_hot encoding trainY = to_categorical(trainY, nb_classes=2) testY = to_categorical(testY, nb_classes=2) # build an embedding network = input_data(shape=[None, 100], name='input') network = tflearn.embedding(network, input_dim=10000, output_dim=128) # build an convnet branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer='L2') branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer='L2') branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer='L2') network = merge([branch1, branch2, branch3], mode='concat', axis=1) network = tf.expand_dims(network, 2) network = global_max_pool(network) network = dropout(network, 0.5) network = fully_connected(network, 2, activation='softmax') network = regression(network, optimizer='adam', learning_rate=0.001, loss='categorical_crossentropy', name='target') # training model = tflearn.DNN(network, tensorboard_verbose=0) model.fit(trainX, trainY, n_epoch=5, shuffle=True, validation_set=(testX, testY), show_metric=True, batch_size=32)
def block8(net, scale=1.0, activation="relu"): tower_conv = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_1x1'))) tower_conv1_0 = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 224, [1,3], bias=False, activation=None, name='Conv2d_0b_1x3'))) tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 256, [3,1], bias=False, name='Conv2d_0c_3x1'))) tower_mixed = merge([tower_conv,tower_conv1_2], mode='concat', axis=3) tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1'))) net += scale * tower_out if activation: if isinstance(activation, str): net = activations.get(activation)(net) elif hasattr(activation, '__call__'): net = activation(net) else: raise ValueError("Invalid Activation.") return net
def do_cnn_word2vec_2d_345(trainX, testX, trainY, testY): global max_features global max_document_length print "CNN and word2vec_2d_345" y_test = testY trainY = to_categorical(trainY, nb_classes=2) testY = to_categorical(testY, nb_classes=2) # Building convolutional network network = input_data(shape=[None,max_document_length,max_features,1], name='input') network = tflearn.embedding(network, input_dim=1, output_dim=128,validate_indices=False) branch1 = conv_2d(network, 128, 3, padding='valid', activation='relu', regularizer="L2") branch2 = conv_2d(network, 128, 4, padding='valid', activation='relu', regularizer="L2") branch3 = conv_2d(network, 128, 5, padding='valid', activation='relu', regularizer="L2") network = merge([branch1, branch2, branch3], mode='concat', axis=1) network = tf.expand_dims(network, 2) network = global_max_pool_2d(network) network = dropout(network, 0.8) network = fully_connected(network, 2, activation='softmax') network = regression(network, optimizer='adam', learning_rate=0.001, loss='categorical_crossentropy', name='target') # Training model = tflearn.DNN(network, tensorboard_verbose=0) model.fit(trainX, trainY, n_epoch=5, shuffle=True, validation_set=(testX, testY), show_metric=True, batch_size=100,run_id="sms") y_predict_list = model.predict(testX) print y_predict_list y_predict = [] for i in y_predict_list: print i[0] if i[0] > 0.5: y_predict.append(0) else: y_predict.append(1) print(classification_report(y_test, y_predict)) print metrics.confusion_matrix(y_test, y_predict)
def _model5(): global yTest, img_aug tf.reset_default_graph() img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() def block35(net, scale=1.0, activation="relu"): tower_conv = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None, name='Conv2d_1x1'))) tower_conv1_0 = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None,name='Conv2d_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 32, 3, bias=False, activation=None,name='Conv2d_0b_3x3'))) tower_conv2_0 = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None, name='Conv2d_0a_1x1'))) tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2_0, 48,3, bias=False, activation=None, name='Conv2d_0b_3x3'))) tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 64,3, bias=False, activation=None, name='Conv2d_0c_3x3'))) tower_mixed = merge([tower_conv, tower_conv1_1, tower_conv2_2], mode='concat', axis=3) tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1'))) net += scale * tower_out if activation: if isinstance(activation, str): net = activations.get(activation)(net) elif hasattr(activation, '__call__'): net = activation(net) else: raise ValueError("Invalid Activation.") return net def block17(net, scale=1.0, activation="relu"): tower_conv = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_1x1'))) tower_conv_1_0 = relu(batch_normalization(conv_2d(net, 128, 1, bias=False, activation=None, name='Conv2d_0a_1x1'))) tower_conv_1_1 = relu(batch_normalization(conv_2d(tower_conv_1_0, 160,[1,7], bias=False, activation=None,name='Conv2d_0b_1x7'))) tower_conv_1_2 = relu(batch_normalization(conv_2d(tower_conv_1_1, 192, [7,1], bias=False, activation=None,name='Conv2d_0c_7x1'))) tower_mixed = merge([tower_conv,tower_conv_1_2], mode='concat', axis=3) tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1'))) net += scale * tower_out if activation: if isinstance(activation, str): net = activations.get(activation)(net) elif hasattr(activation, '__call__'): net = activation(net) else: raise ValueError("Invalid Activation.") return net def block8(net, scale=1.0, activation="relu"): tower_conv = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_1x1'))) tower_conv1_0 = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 224, [1,3], bias=False, activation=None, name='Conv2d_0b_1x3'))) tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 256, [3,1], bias=False, name='Conv2d_0c_3x1'))) tower_mixed = merge([tower_conv,tower_conv1_2], mode='concat', axis=3) tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1'))) net += scale * tower_out if activation: if isinstance(activation, str): net = activations.get(activation)(net) elif hasattr(activation, '__call__'): net = activation(net) else: raise ValueError("Invalid Activation.") return net num_classes = len(Y[0]) dropout_keep_prob = 0.8 network = input_data(shape=[None, inputSize, inputSize, dim], name='input', data_preprocessing=img_prep, data_augmentation=img_aug) conv1a_3_3 = relu(batch_normalization(conv_2d(network, 32, 3, strides=2, bias=False, padding='VALID',activation=None,name='Conv2d_1a_3x3'))) conv2a_3_3 = relu(batch_normalization(conv_2d(conv1a_3_3, 32, 3, bias=False, padding='VALID',activation=None, name='Conv2d_2a_3x3'))) conv2b_3_3 = relu(batch_normalization(conv_2d(conv2a_3_3, 64, 3, bias=False, activation=None, name='Conv2d_2b_3x3'))) maxpool3a_3_3 = max_pool_2d(conv2b_3_3, 3, strides=2, padding='VALID', name='MaxPool_3a_3x3') conv3b_1_1 = relu(batch_normalization(conv_2d(maxpool3a_3_3, 80, 1, bias=False, padding='VALID',activation=None, name='Conv2d_3b_1x1'))) conv4a_3_3 = relu(batch_normalization(conv_2d(conv3b_1_1, 192, 3, bias=False, padding='VALID',activation=None, name='Conv2d_4a_3x3'))) maxpool5a_3_3 = max_pool_2d(conv4a_3_3, 3, strides=2, padding='VALID', name='MaxPool_5a_3x3') tower_conv = relu(batch_normalization(conv_2d(maxpool5a_3_3, 96, 1, bias=False, activation=None, name='Conv2d_5b_b0_1x1'))) tower_conv1_0 = relu(batch_normalization(conv_2d(maxpool5a_3_3, 48, 1, bias=False, activation=None, name='Conv2d_5b_b1_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 64, 5, bias=False, activation=None, name='Conv2d_5b_b1_0b_5x5'))) tower_conv2_0 = relu(batch_normalization(conv_2d(maxpool5a_3_3, 64, 1, bias=False, activation=None, name='Conv2d_5b_b2_0a_1x1'))) tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2_0, 96, 3, bias=False, activation=None, name='Conv2d_5b_b2_0b_3x3'))) tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 96, 3, bias=False, activation=None,name='Conv2d_5b_b2_0c_3x3'))) tower_pool3_0 = avg_pool_2d(maxpool5a_3_3, 3, strides=1, padding='same', name='AvgPool_5b_b3_0a_3x3') tower_conv3_1 = relu(batch_normalization(conv_2d(tower_pool3_0, 64, 1, bias=False, activation=None,name='Conv2d_5b_b3_0b_1x1'))) tower_5b_out = merge([tower_conv, tower_conv1_1, tower_conv2_2, tower_conv3_1], mode='concat', axis=3) net = repeat(tower_5b_out, 10, block35, scale=0.17) ''' tower_conv = relu(batch_normalization(conv_2d(net, 384, 3, bias=False, strides=2,activation=None, padding='VALID', name='Conv2d_6a_b0_0a_3x3'))) tower_conv1_0 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 256, 3, bias=False, activation=None, name='Conv2d_6a_b1_0b_3x3'))) tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 384, 3, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_6a_b1_0c_3x3'))) tower_pool = max_pool_2d(net, 3, strides=2, padding='VALID',name='MaxPool_1a_3x3') net = merge([tower_conv, tower_conv1_2, tower_pool], mode='concat', axis=3) net = repeat(net, 20, block17, scale=0.1) tower_conv = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_0a_1x1'))) tower_conv0_1 = relu(batch_normalization(conv_2d(tower_conv, 384, 3, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_0a_1x1'))) tower_conv1 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, padding='VALID', activation=None,name='Conv2d_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1,288,3, bias=False, strides=2, padding='VALID',activation=None, name='COnv2d_1a_3x3'))) tower_conv2 = relu(batch_normalization(conv_2d(net, 256,1, bias=False, activation=None,name='Conv2d_0a_1x1'))) tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2, 288,3, bias=False, name='Conv2d_0b_3x3',activation=None))) tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 320, 3, bias=False, strides=2, padding='VALID',activation=None, name='Conv2d_1a_3x3'))) tower_pool = max_pool_2d(net, 3, strides=2, padding='VALID', name='MaxPool_1a_3x3') ''' tower_conv = relu(batch_normalization(conv_2d(net, 384, 1, bias=False, strides=2,activation=None, padding='VALID', name='Conv2d_6a_b0_0a_3x3'))) tower_conv1_0 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0b_3x3'))) tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 384, 1, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_6a_b1_0c_3x3'))) tower_pool = max_pool_2d(net, 1, strides=2, padding='VALID',name='MaxPool_1a_3x3') net = merge([tower_conv, tower_conv1_2, tower_pool], mode='concat', axis=3) net = repeat(net, 20, block17, scale=0.1) tower_conv = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_0a_1x1'))) tower_conv0_1 = relu(batch_normalization(conv_2d(tower_conv, 384, 1, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_0a_1x1'))) tower_conv1 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, padding='VALID', activation=None,name='Conv2d_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1,288,1, bias=False, strides=2, padding='VALID',activation=None, name='COnv2d_1a_3x3'))) tower_conv2 = relu(batch_normalization(conv_2d(net, 256,1, bias=False, activation=None,name='Conv2d_0a_1x1'))) tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2, 288,1, bias=False, name='Conv2d_0b_3x3',activation=None))) tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 320, 1, bias=False, strides=2, padding='VALID',activation=None, name='Conv2d_1a_3x3'))) tower_pool = max_pool_2d(net, 1, strides=2, padding='VALID', name='MaxPool_1a_3x3') #### net = merge([tower_conv0_1, tower_conv1_1,tower_conv2_2, tower_pool], mode='concat', axis=3) net = repeat(net, 9, block8, scale=0.2) net = block8(net, activation=None) net = relu(batch_normalization(conv_2d(net, 1536, 1, bias=False, activation=None, name='Conv2d_7b_1x1'))) net = avg_pool_2d(net, net.get_shape().as_list()[1:3],strides=2, padding='VALID', name='AvgPool_1a_8x8') net = flatten(net) net = dropout(net, dropout_keep_prob) loss = fully_connected(net, num_classes,activation='softmax') network = tflearn.regression(loss, optimizer='RMSprop', loss='categorical_crossentropy', learning_rate=0.0001) model = tflearn.DNN(network, checkpoint_path='inception_resnet_v2', max_checkpoints=1, tensorboard_verbose=2, tensorboard_dir="./tflearn_logs/") model.fit(X, Y, n_epoch=epochNum, validation_set=(xTest, yTest), shuffle=True, show_metric=True, batch_size=batchNum, snapshot_step=2000, snapshot_epoch=False, run_id='inception_resnet_v2_oxflowers17') if modelStore: model.save(_id + '-model.tflearn')
conv4a_3_3 = relu(batch_normalization(conv_2d(conv3b_1_1, 192, 3, bias=False, padding='VALID',activation=None, name='Conv2d_4a_3x3'))) maxpool5a_3_3 = max_pool_2d(conv4a_3_3, 3, strides=2, padding='VALID', name='MaxPool_5a_3x3') tower_conv = relu(batch_normalization(conv_2d(maxpool5a_3_3, 96, 1, bias=False, activation=None, name='Conv2d_5b_b0_1x1'))) tower_conv1_0 = relu(batch_normalization(conv_2d(maxpool5a_3_3, 48, 1, bias=False, activation=None, name='Conv2d_5b_b1_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 64, 5, bias=False, activation=None, name='Conv2d_5b_b1_0b_5x5'))) tower_conv2_0 = relu(batch_normalization(conv_2d(maxpool5a_3_3, 64, 1, bias=False, activation=None, name='Conv2d_5b_b2_0a_1x1'))) tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2_0, 96, 3, bias=False, activation=None, name='Conv2d_5b_b2_0b_3x3'))) tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 96, 3, bias=False, activation=None,name='Conv2d_5b_b2_0c_3x3'))) tower_pool3_0 = avg_pool_2d(maxpool5a_3_3, 3, strides=1, padding='same', name='AvgPool_5b_b3_0a_3x3') tower_conv3_1 = relu(batch_normalization(conv_2d(tower_pool3_0, 64, 1, bias=False, activation=None,name='Conv2d_5b_b3_0b_1x1'))) tower_5b_out = merge([tower_conv, tower_conv1_1, tower_conv2_2, tower_conv3_1], mode='concat', axis=3) net = repeat(tower_5b_out, 10, block35, scale=0.17) tower_conv = relu(batch_normalization(conv_2d(net, 384, 3, bias=False, strides=2,activation=None, padding='VALID', name='Conv2d_6a_b0_0a_3x3'))) tower_conv1_0 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 256, 3, bias=False, activation=None, name='Conv2d_6a_b1_0b_3x3'))) tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 384, 3, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_6a_b1_0c_3x3'))) tower_pool = max_pool_2d(net, 3, strides=2, padding='VALID',name='MaxPool_1a_3x3') net = merge([tower_conv, tower_conv1_2, tower_pool], mode='concat', axis=3) net = repeat(net, 20, block17, scale=0.1) tower_conv = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_0a_1x1'))) tower_conv0_1 = relu(batch_normalization(conv_2d(tower_conv, 384, 3, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_0a_1x1'))) tower_conv1 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, padding='VALID', activation=None,name='Conv2d_0a_1x1')))
def _model5(): global yTest, img_aug tf.reset_default_graph() img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() def block35(net, scale=1.0, activation="relu"): tower_conv = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None, name='Conv2d_1x1'))) tower_conv1_0 = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None,name='Conv2d_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 32, 3, bias=False, activation=None,name='Conv2d_0b_3x3'))) tower_conv2_0 = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None, name='Conv2d_0a_1x1'))) tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2_0, 48,3, bias=False, activation=None, name='Conv2d_0b_3x3'))) tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 64,3, bias=False, activation=None, name='Conv2d_0c_3x3'))) tower_mixed = merge([tower_conv, tower_conv1_1, tower_conv2_2], mode='concat', axis=3) tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1'))) net += scale * tower_out if activation: if isinstance(activation, str): net = activations.get(activation)(net) elif hasattr(activation, '__call__'): net = activation(net) else: raise ValueError("Invalid Activation.") return net def block17(net, scale=1.0, activation="relu"): tower_conv = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_1x1'))) tower_conv_1_0 = relu(batch_normalization(conv_2d(net, 128, 1, bias=False, activation=None, name='Conv2d_0a_1x1'))) tower_conv_1_1 = relu(batch_normalization(conv_2d(tower_conv_1_0, 160,[1,7], bias=False, activation=None,name='Conv2d_0b_1x7'))) tower_conv_1_2 = relu(batch_normalization(conv_2d(tower_conv_1_1, 192, [7,1], bias=False, activation=None,name='Conv2d_0c_7x1'))) tower_mixed = merge([tower_conv,tower_conv_1_2], mode='concat', axis=3) tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1'))) net += scale * tower_out if activation: if isinstance(activation, str): net = activations.get(activation)(net) elif hasattr(activation, '__call__'): net = activation(net) else: raise ValueError("Invalid Activation.") return net def block8(net, scale=1.0, activation="relu"): tower_conv = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_1x1'))) tower_conv1_0 = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 224, [1,3], bias=False, activation=None, name='Conv2d_0b_1x3'))) tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 256, [3,1], bias=False, name='Conv2d_0c_3x1'))) tower_mixed = merge([tower_conv,tower_conv1_2], mode='concat', axis=3) tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1'))) net += scale * tower_out if activation: if isinstance(activation, str): net = activations.get(activation)(net) elif hasattr(activation, '__call__'): net = activation(net) else: raise ValueError("Invalid Activation.") return net num_classes = len(yTest[0]) dropout_keep_prob = 0.8 network = input_data(shape=[None, inputSize, inputSize, dim], name='input', data_preprocessing=img_prep, data_augmentation=img_aug) conv1a_3_3 = relu(batch_normalization(conv_2d(network, 32, 3, strides=2, bias=False, padding='VALID',activation=None,name='Conv2d_1a_3x3'))) conv2a_3_3 = relu(batch_normalization(conv_2d(conv1a_3_3, 32, 3, bias=False, padding='VALID',activation=None, name='Conv2d_2a_3x3'))) conv2b_3_3 = relu(batch_normalization(conv_2d(conv2a_3_3, 64, 3, bias=False, activation=None, name='Conv2d_2b_3x3'))) maxpool3a_3_3 = max_pool_2d(conv2b_3_3, 3, strides=2, padding='VALID', name='MaxPool_3a_3x3') conv3b_1_1 = relu(batch_normalization(conv_2d(maxpool3a_3_3, 80, 1, bias=False, padding='VALID',activation=None, name='Conv2d_3b_1x1'))) conv4a_3_3 = relu(batch_normalization(conv_2d(conv3b_1_1, 192, 3, bias=False, padding='VALID',activation=None, name='Conv2d_4a_3x3'))) maxpool5a_3_3 = max_pool_2d(conv4a_3_3, 3, strides=2, padding='VALID', name='MaxPool_5a_3x3') tower_conv = relu(batch_normalization(conv_2d(maxpool5a_3_3, 96, 1, bias=False, activation=None, name='Conv2d_5b_b0_1x1'))) tower_conv1_0 = relu(batch_normalization(conv_2d(maxpool5a_3_3, 48, 1, bias=False, activation=None, name='Conv2d_5b_b1_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 64, 5, bias=False, activation=None, name='Conv2d_5b_b1_0b_5x5'))) tower_conv2_0 = relu(batch_normalization(conv_2d(maxpool5a_3_3, 64, 1, bias=False, activation=None, name='Conv2d_5b_b2_0a_1x1'))) tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2_0, 96, 3, bias=False, activation=None, name='Conv2d_5b_b2_0b_3x3'))) tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 96, 3, bias=False, activation=None,name='Conv2d_5b_b2_0c_3x3'))) tower_pool3_0 = avg_pool_2d(maxpool5a_3_3, 3, strides=1, padding='same', name='AvgPool_5b_b3_0a_3x3') tower_conv3_1 = relu(batch_normalization(conv_2d(tower_pool3_0, 64, 1, bias=False, activation=None,name='Conv2d_5b_b3_0b_1x1'))) tower_5b_out = merge([tower_conv, tower_conv1_1, tower_conv2_2, tower_conv3_1], mode='concat', axis=3) net = repeat(tower_5b_out, 10, block35, scale=0.17) ''' tower_conv = relu(batch_normalization(conv_2d(net, 384, 3, bias=False, strides=2,activation=None, padding='VALID', name='Conv2d_6a_b0_0a_3x3'))) tower_conv1_0 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 256, 3, bias=False, activation=None, name='Conv2d_6a_b1_0b_3x3'))) tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 384, 3, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_6a_b1_0c_3x3'))) tower_pool = max_pool_2d(net, 3, strides=2, padding='VALID',name='MaxPool_1a_3x3') net = merge([tower_conv, tower_conv1_2, tower_pool], mode='concat', axis=3) net = repeat(net, 20, block17, scale=0.1) tower_conv = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_0a_1x1'))) tower_conv0_1 = relu(batch_normalization(conv_2d(tower_conv, 384, 3, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_0a_1x1'))) tower_conv1 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, padding='VALID', activation=None,name='Conv2d_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1,288,3, bias=False, strides=2, padding='VALID',activation=None, name='COnv2d_1a_3x3'))) tower_conv2 = relu(batch_normalization(conv_2d(net, 256,1, bias=False, activation=None,name='Conv2d_0a_1x1'))) tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2, 288,3, bias=False, name='Conv2d_0b_3x3',activation=None))) tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 320, 3, bias=False, strides=2, padding='VALID',activation=None, name='Conv2d_1a_3x3'))) tower_pool = max_pool_2d(net, 3, strides=2, padding='VALID', name='MaxPool_1a_3x3') ''' tower_conv = relu(batch_normalization(conv_2d(net, 384, 1, bias=False, strides=2,activation=None, padding='VALID', name='Conv2d_6a_b0_0a_3x3'))) tower_conv1_0 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0b_3x3'))) tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 384, 1, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_6a_b1_0c_3x3'))) tower_pool = max_pool_2d(net, 1, strides=2, padding='VALID',name='MaxPool_1a_3x3') net = merge([tower_conv, tower_conv1_2, tower_pool], mode='concat', axis=3) net = repeat(net, 20, block17, scale=0.1) tower_conv = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_0a_1x1'))) tower_conv0_1 = relu(batch_normalization(conv_2d(tower_conv, 384, 1, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_0a_1x1'))) tower_conv1 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, padding='VALID', activation=None,name='Conv2d_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1,288,1, bias=False, strides=2, padding='VALID',activation=None, name='COnv2d_1a_3x3'))) tower_conv2 = relu(batch_normalization(conv_2d(net, 256,1, bias=False, activation=None,name='Conv2d_0a_1x1'))) tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2, 288,1, bias=False, name='Conv2d_0b_3x3',activation=None))) tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 320, 1, bias=False, strides=2, padding='VALID',activation=None, name='Conv2d_1a_3x3'))) tower_pool = max_pool_2d(net, 1, strides=2, padding='VALID', name='MaxPool_1a_3x3') #### net = merge([tower_conv0_1, tower_conv1_1,tower_conv2_2, tower_pool], mode='concat', axis=3) net = repeat(net, 9, block8, scale=0.2) net = block8(net, activation=None) net = relu(batch_normalization(conv_2d(net, 1536, 1, bias=False, activation=None, name='Conv2d_7b_1x1'))) net = avg_pool_2d(net, net.get_shape().as_list()[1:3],strides=2, padding='VALID', name='AvgPool_1a_8x8') net = flatten(net) net = dropout(net, dropout_keep_prob) loss = fully_connected(net, num_classes,activation='softmax') network = tflearn.regression(loss, optimizer='RMSprop', loss='categorical_crossentropy', learning_rate=0.0001) model = tflearn.DNN(network, checkpoint_path='inception_resnet_v2', max_checkpoints=1, tensorboard_verbose=2, tensorboard_dir="./tflearn_logs/") model.load(_path) pred = model.predict(xTest) df = pd.DataFrame(pred) df.to_csv(_path + ".csv") newList = pred.copy() newList = convert2(newList) if _CSV: makeCSV(newList) pred = convert2(pred) pred = convert3(pred) yTest = convert3(yTest) print(metrics.confusion_matrix(yTest, pred)) print(metrics.classification_report(yTest, pred)) print('Accuracy', accuracy_score(yTest, pred)) print() if _wrFile: writeTest(pred)
def _model4(): # Taken from TFLearn examples and based on Googles Inception. DO NOT RUN!!!! global yTest, img_aug tf.reset_default_graph() img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() network = input_data(shape=[None, inputSize, inputSize, dim], name='input', data_preprocessing=img_prep, data_augmentation=img_aug) conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu', name='conv1_7_7_s2') pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2) pool1_3_3 = local_response_normalization(pool1_3_3) conv2_3_3_reduce = conv_2d(pool1_3_3, 64, 1, activation='relu', name='conv2_3_3_reduce') conv2_3_3 = conv_2d(conv2_3_3_reduce, 192, 3, activation='relu', name='conv2_3_3') conv2_3_3 = local_response_normalization(conv2_3_3) pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2') # 3a inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1') inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96, 1, activation='relu', name='inception_3a_3_3_reduce') inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128, filter_size=3, activation='relu', name='inception_3a_3_3') inception_3a_5_5_reduce = conv_2d(pool2_3_3, 16, filter_size=1, activation='relu', name='inception_3a_5_5_reduce') inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name='inception_3a_5_5') inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, name='inception_3a_pool') inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1') inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=3) # 3b inception_3b_1_1 = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_1_1') inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce') inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu', name='inception_3b_3_3') inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu', name='inception_3b_5_5_reduce') inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name='inception_3b_5_5') inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool') inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1, activation='relu', name='inception_3b_pool_1_1') inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat', axis=3, name='inception_3b_output') pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3') # 4a inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1') inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce') inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3') inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce') inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5') inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool') inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1') inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=3, name='inception_4a_output') # 4b inception_4b_1_1 = conv_2d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1') inception_4b_3_3_reduce = conv_2d(inception_4a_output, 112, filter_size=1, activation='relu', name='inception_4b_3_3_reduce') inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3') inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce') inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4b_5_5') inception_4b_pool = max_pool_2d(inception_4a_output, kernel_size=3, strides=1, name='inception_4b_pool') inception_4b_pool_1_1 = conv_2d(inception_4b_pool, 64, filter_size=1, activation='relu', name='inception_4b_pool_1_1') inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1], mode='concat', axis=3, name='inception_4b_output') # 4c inception_4c_1_1 = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_1_1') inception_4c_3_3_reduce = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_3_3_reduce') inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce, 256, filter_size=3, activation='relu', name='inception_4c_3_3') inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce') inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4c_5_5') inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3, strides=1) inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1, activation='relu', name='inception_4c_pool_1_1') inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1], mode='concat', axis=3, name='inception_4c_output') # 4d inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1') inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce') inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3') inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce') inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4d_5_5') inception_4d_pool = max_pool_2d(inception_4c_output, kernel_size=3, strides=1, name='inception_4d_pool') inception_4d_pool_1_1 = conv_2d(inception_4d_pool, 64, filter_size=1, activation='relu', name='inception_4d_pool_1_1') inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1], mode='concat', axis=3, name='inception_4d_output') # 4e inception_4e_1_1 = conv_2d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1') inception_4e_3_3_reduce = conv_2d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce') inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3') inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce') inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_4e_5_5') inception_4e_pool = max_pool_2d(inception_4d_output, kernel_size=3, strides=1, name='inception_4e_pool') inception_4e_pool_1_1 = conv_2d(inception_4e_pool, 128, filter_size=1, activation='relu', name='inception_4e_pool_1_1') inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5, inception_4e_pool_1_1], axis=3, mode='concat') pool4_3_3 = max_pool_2d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3') # 5a inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1') inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce') inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3') inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce') inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5a_5_5') inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool') inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1, activation='relu', name='inception_5a_pool_1_1') inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=3, mode='concat') # 5b inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1, activation='relu', name='inception_5b_1_1') inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192, filter_size=1, activation='relu', name='inception_5b_3_3_reduce') inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3, activation='relu', name='inception_5b_3_3') inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48, filter_size=1, activation='relu', name='inception_5b_5_5_reduce') inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5b_5_5') inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool') inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1, activation='relu', name='inception_5b_pool_1_1') inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=3, mode='concat') pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1) pool5_7_7 = dropout(pool5_7_7, 0.4) # fc loss = fully_connected(pool5_7_7, len(yTest[0]), activation='softmax') network = regression(loss, optimizer='momentum', loss='categorical_crossentropy', learning_rate=0.001) # to train model = tflearn.DNN(network, checkpoint_path='model_googlenet', max_checkpoints=1, tensorboard_verbose=2) model.load(_path) pred = model.predict(xTest) df = pd.DataFrame(pred) df.to_csv(_path + ".csv") newList = pred.copy() newList = convert2(newList) if _CSV: makeCSV(newList) pred = convert2(pred) pred = convert3(pred) yTest = convert3(yTest) print(metrics.confusion_matrix(yTest, pred)) print(metrics.classification_report(yTest, pred)) print('Accuracy', accuracy_score(yTest, pred)) print() if _wrFile: writeTest(pred)
# pickle.dump((trainX,trainY,testX,testY,vocab_size),f) #else: # print("traning data exists in cache. going to use it.") # 3.Building convolutional network ######################################MODEL:1.conv-2.conv-3.conv-4.max_pool-5.dropout-6.FC############################################################################################## #(shape=None, placeholder=None, dtype=tf.float32,data_preprocessing=None, data_augmentation=None,name="InputData") network = input_data(shape=[None, 100], name='input') #[None, 100] `input_data` is used as a data entry (placeholder) of a network. This placeholder will be feeded with data when training network = tflearn.embedding(network, input_dim=vocab_size, output_dim=128) #TODO [None, 100,128].embedding layer for a sequence of ids. network: Incoming 2-D Tensor. input_dim: vocabulary size, oput_dim:embedding size #conv_1d(incoming,nb_filter,filter_size) branch1 = conv_1d(network, 128, 1, padding='valid', activation='relu', regularizer="L2") branch2 = conv_1d(network, 128, 2, padding='valid', activation='relu', regularizer="L2") branch3 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2") # [batch_size, new steps1, nb_filters]. padding:"VALID",only ever drops the right-most columns branch4 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2") # [batch_size, new steps2, nb_filters] branch5 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2") # [batch_size, new steps3, nb_filters] network = merge([branch1, branch2, branch3,branch4,branch5], mode='concat', axis=1) # merge a list of `Tensor` into a single one.===>[batch_size, new steps1+new step2+new step3, nb_filters] network = tf.expand_dims(network, 2) #[batch_size, new steps1+new step2+new step3,1, nb_filters] Inserts a dimension of 1 into a tensor's shape network = global_max_pool(network) #[batch_size, pooled dim] network = dropout(network, 0.5) #[batch_size, pooled dim] network = fully_connected(network, number_classes, activation='softmax') #matmul([batch_size, pooled_dim],[pooled_dim,2])---->[batch_size,number_classes] top5 = tflearn.metrics.Top_k(k=5) network = regression(network, optimizer='adam', learning_rate=0.001,loss='categorical_crossentropy', name='target') #metric=top5 ######################################MODEL:1.conv-2.conv-3.conv-4.max_pool-5.dropout-6.FC################################################################################################ # 4.Training model = tflearn.DNN(network, tensorboard_verbose=0) #model.fit(trainX, trainY, n_epoch = 10, shuffle=True, validation_set=(testX, testY), show_metric=True, batch_size=256) #32 #model.save('model_zhihu_cnn12345') model.load('model_zhihu_cnn12345') print("going to make a prediction...") predict_result=model.predict(testX[0:1000]) print("predict_result:",predict_result)
def _model4(): # Taken from TFLearn examples and based on Googles Inception. DO NOT RUN!!!! global yTest, img_aug tf.reset_default_graph() img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() network = input_data(shape=[None, inputSize, inputSize, dim], name='input', data_preprocessing=img_prep, data_augmentation=img_aug) conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu', name='conv1_7_7_s2') pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2) pool1_3_3 = local_response_normalization(pool1_3_3) conv2_3_3_reduce = conv_2d(pool1_3_3, 64, 1, activation='relu', name='conv2_3_3_reduce') conv2_3_3 = conv_2d(conv2_3_3_reduce, 192, 3, activation='relu', name='conv2_3_3') conv2_3_3 = local_response_normalization(conv2_3_3) pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2') # 3a inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1') inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96, 1, activation='relu', name='inception_3a_3_3_reduce') inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128, filter_size=3, activation='relu', name='inception_3a_3_3') inception_3a_5_5_reduce = conv_2d(pool2_3_3, 16, filter_size=1, activation='relu', name='inception_3a_5_5_reduce') inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name='inception_3a_5_5') inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, name='inception_3a_pool') inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1') inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=3) # 3b inception_3b_1_1 = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_1_1') inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce') inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu', name='inception_3b_3_3') inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu', name='inception_3b_5_5_reduce') inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name='inception_3b_5_5') inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool') inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1, activation='relu', name='inception_3b_pool_1_1') inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat', axis=3, name='inception_3b_output') pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3') # 4a inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1') inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce') inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3') inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce') inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5') inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool') inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1') inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=3, name='inception_4a_output') # 4b inception_4b_1_1 = conv_2d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1') inception_4b_3_3_reduce = conv_2d(inception_4a_output, 112, filter_size=1, activation='relu', name='inception_4b_3_3_reduce') inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3') inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce') inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4b_5_5') inception_4b_pool = max_pool_2d(inception_4a_output, kernel_size=3, strides=1, name='inception_4b_pool') inception_4b_pool_1_1 = conv_2d(inception_4b_pool, 64, filter_size=1, activation='relu', name='inception_4b_pool_1_1') inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1], mode='concat', axis=3, name='inception_4b_output') # 4c inception_4c_1_1 = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_1_1') inception_4c_3_3_reduce = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_3_3_reduce') inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce, 256, filter_size=3, activation='relu', name='inception_4c_3_3') inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce') inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4c_5_5') inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3, strides=1) inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1, activation='relu', name='inception_4c_pool_1_1') inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1], mode='concat', axis=3, name='inception_4c_output') # 4d inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1') inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce') inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3') inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce') inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4d_5_5') inception_4d_pool = max_pool_2d(inception_4c_output, kernel_size=3, strides=1, name='inception_4d_pool') inception_4d_pool_1_1 = conv_2d(inception_4d_pool, 64, filter_size=1, activation='relu', name='inception_4d_pool_1_1') inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1], mode='concat', axis=3, name='inception_4d_output') # 4e inception_4e_1_1 = conv_2d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1') inception_4e_3_3_reduce = conv_2d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce') inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3') inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce') inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_4e_5_5') inception_4e_pool = max_pool_2d(inception_4d_output, kernel_size=3, strides=1, name='inception_4e_pool') inception_4e_pool_1_1 = conv_2d(inception_4e_pool, 128, filter_size=1, activation='relu', name='inception_4e_pool_1_1') inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5, inception_4e_pool_1_1], axis=3, mode='concat') pool4_3_3 = max_pool_2d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3') # 5a inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1') inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce') inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3') inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce') inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5a_5_5') inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool') inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1, activation='relu', name='inception_5a_pool_1_1') inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=3, mode='concat') # 5b inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1, activation='relu', name='inception_5b_1_1') inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192, filter_size=1, activation='relu', name='inception_5b_3_3_reduce') inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3, activation='relu', name='inception_5b_3_3') inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48, filter_size=1, activation='relu', name='inception_5b_5_5_reduce') inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5b_5_5') inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool') inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1, activation='relu', name='inception_5b_pool_1_1') inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=3, mode='concat') pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1) pool5_7_7 = dropout(pool5_7_7, 0.4) # fc loss = fully_connected(pool5_7_7, len(Y[0]), activation='softmax') network = regression(loss, optimizer='momentum', loss='categorical_crossentropy', learning_rate=0.001) # to train model = tflearn.DNN(network, checkpoint_path='model_googlenet', max_checkpoints=1, tensorboard_verbose=2) model.fit(X, Y, n_epoch=epochNum, validation_set=(xTest, yTest), shuffle=True, show_metric=True, batch_size=batchNum, snapshot_step=200, snapshot_epoch=False, run_id='googlenet_oxflowers17') if modelStore: model.save(_id + '-model.tflearn')
def build_le_network(self, network): """ Build google net. (google inception v3) Args: network: Returns: """ from tflearn.layers.merge_ops import merge conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu', name='conv1_7_7_s2') pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2) pool1_3_3 = local_response_normalization(pool1_3_3) conv2_3_3_reduce = conv_2d(pool1_3_3, 64, 1, activation='relu', name='conv2_3_3_reduce') conv2_3_3 = conv_2d(conv2_3_3_reduce, 192, 3, activation='relu', name='conv2_3_3') conv2_3_3 = local_response_normalization(conv2_3_3) pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2') inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1') inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96, 1, activation='relu', name='inception_3a_3_3_reduce') inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128, filter_size=3, activation='relu', name='inception_3a_3_3') inception_3a_5_5_reduce = conv_2d(pool2_3_3, 16, filter_size=1, activation='relu', name='inception_3a_5_5_reduce') inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name='inception_3a_5_5') inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, ) inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1') # merge the inception_3a__ inception_3a_output = merge( [inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=3) inception_3b_1_1 = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_1_1') inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce') inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu', name='inception_3b_3_3') inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu', name='inception_3b_5_5_reduce') inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name='inception_3b_5_5') inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool') inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1, activation='relu', name='inception_3b_pool_1_1') # merge the inception_3b_* inception_3b_output = merge( [inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat', axis=3, name='inception_3b_output') pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3') inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1') inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce') inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3') inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce') inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5') inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool') inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1') inception_4a_output = merge( [inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=3, name='inception_4a_output') inception_4b_1_1 = conv_2d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1') inception_4b_3_3_reduce = conv_2d(inception_4a_output, 112, filter_size=1, activation='relu', name='inception_4b_3_3_reduce') inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3') inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce') inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4b_5_5') inception_4b_pool = max_pool_2d(inception_4a_output, kernel_size=3, strides=1, name='inception_4b_pool') inception_4b_pool_1_1 = conv_2d(inception_4b_pool, 64, filter_size=1, activation='relu', name='inception_4b_pool_1_1') inception_4b_output = merge( [inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1], mode='concat', axis=3, name='inception_4b_output') inception_4c_1_1 = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_1_1') inception_4c_3_3_reduce = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_3_3_reduce') inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce, 256, filter_size=3, activation='relu', name='inception_4c_3_3') inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce') inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4c_5_5') inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3, strides=1) inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1, activation='relu', name='inception_4c_pool_1_1') inception_4c_output = merge( [inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1], mode='concat', axis=3, name='inception_4c_output') inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1') inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce') inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3') inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce') inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4d_5_5') inception_4d_pool = max_pool_2d(inception_4c_output, kernel_size=3, strides=1, name='inception_4d_pool') inception_4d_pool_1_1 = conv_2d(inception_4d_pool, 64, filter_size=1, activation='relu', name='inception_4d_pool_1_1') inception_4d_output = merge( [inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1], mode='concat', axis=3, name='inception_4d_output') inception_4e_1_1 = conv_2d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1') inception_4e_3_3_reduce = conv_2d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce') inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3') inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce') inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_4e_5_5') inception_4e_pool = max_pool_2d(inception_4d_output, kernel_size=3, strides=1, name='inception_4e_pool') inception_4e_pool_1_1 = conv_2d(inception_4e_pool, 128, filter_size=1, activation='relu', name='inception_4e_pool_1_1') inception_4e_output = merge( [inception_4e_1_1, inception_4e_3_3, inception_4e_5_5, inception_4e_pool_1_1], axis=3, mode='concat') pool4_3_3 = max_pool_2d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3') inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1') inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce') inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3') inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce') inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5a_5_5') inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool') inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1, activation='relu', name='inception_5a_pool_1_1') inception_5a_output = merge( [inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=3, mode='concat') inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1, activation='relu', name='inception_5b_1_1') inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192, filter_size=1, activation='relu', name='inception_5b_3_3_reduce') inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3, activation='relu', name='inception_5b_3_3') inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48, filter_size=1, activation='relu', name='inception_5b_5_5_reduce') inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5b_5_5') inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool') inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1, activation='relu', name='inception_5b_pool_1_1') inception_5b_output = merge( [inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=3, mode='concat') pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1) pool5_7_7 = dropout(pool5_7_7, 0.4) loss = fully_connected(pool5_7_7, 2, activation='softmax') network = regression(loss, optimizer='momentum', loss='categorical_crossentropy', learning_rate=0.001) return network
def inception_v3(width, height, frame_count, lr, output=9, model_name = 'sentnet_color.model'): network = input_data(shape=[None, width, height,3], name='input') conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu', name = 'conv1_7_7_s2') pool1_3_3 = max_pool_2d(conv1_7_7, 3,strides=2) pool1_3_3 = local_response_normalization(pool1_3_3) conv2_3_3_reduce = conv_2d(pool1_3_3, 64,1, activation='relu',name = 'conv2_3_3_reduce') conv2_3_3 = conv_2d(conv2_3_3_reduce, 192,3, activation='relu', name='conv2_3_3') conv2_3_3 = local_response_normalization(conv2_3_3) pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2') inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1') inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96,1, activation='relu', name='inception_3a_3_3_reduce') inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128,filter_size=3, activation='relu', name = 'inception_3a_3_3') inception_3a_5_5_reduce = conv_2d(pool2_3_3,16, filter_size=1,activation='relu', name ='inception_3a_5_5_reduce' ) inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name= 'inception_3a_5_5') inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, ) inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1') # merge the inception_3a__ inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=3) inception_3b_1_1 = conv_2d(inception_3a_output, 128,filter_size=1,activation='relu', name= 'inception_3b_1_1' ) inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce') inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu',name='inception_3b_3_3') inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu', name = 'inception_3b_5_5_reduce') inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name = 'inception_3b_5_5') inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool') inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1,activation='relu', name='inception_3b_pool_1_1') #merge the inception_3b_* inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat',axis=3,name='inception_3b_output') pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3') inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1') inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce') inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3') inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce') inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5') inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool') inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1') inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=3, name='inception_4a_output') inception_4b_1_1 = conv_2d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1') inception_4b_3_3_reduce = conv_2d(inception_4a_output, 112, filter_size=1, activation='relu', name='inception_4b_3_3_reduce') inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3') inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce') inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4b_5_5') inception_4b_pool = max_pool_2d(inception_4a_output, kernel_size=3, strides=1, name='inception_4b_pool') inception_4b_pool_1_1 = conv_2d(inception_4b_pool, 64, filter_size=1, activation='relu', name='inception_4b_pool_1_1') inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1], mode='concat', axis=3, name='inception_4b_output') inception_4c_1_1 = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu',name='inception_4c_1_1') inception_4c_3_3_reduce = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_3_3_reduce') inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce, 256, filter_size=3, activation='relu', name='inception_4c_3_3') inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce') inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4c_5_5') inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3, strides=1) inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1, activation='relu', name='inception_4c_pool_1_1') inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1], mode='concat', axis=3,name='inception_4c_output') inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1') inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce') inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3') inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce') inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4d_5_5') inception_4d_pool = max_pool_2d(inception_4c_output, kernel_size=3, strides=1, name='inception_4d_pool') inception_4d_pool_1_1 = conv_2d(inception_4d_pool, 64, filter_size=1, activation='relu', name='inception_4d_pool_1_1') inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1], mode='concat', axis=3, name='inception_4d_output') inception_4e_1_1 = conv_2d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1') inception_4e_3_3_reduce = conv_2d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce') inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3') inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce') inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_4e_5_5') inception_4e_pool = max_pool_2d(inception_4d_output, kernel_size=3, strides=1, name='inception_4e_pool') inception_4e_pool_1_1 = conv_2d(inception_4e_pool, 128, filter_size=1, activation='relu', name='inception_4e_pool_1_1') inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5,inception_4e_pool_1_1],axis=3, mode='concat') pool4_3_3 = max_pool_2d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3') inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1') inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce') inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3') inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce') inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5a_5_5') inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool') inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1,activation='relu', name='inception_5a_pool_1_1') inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=3,mode='concat') inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1,activation='relu', name='inception_5b_1_1') inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192, filter_size=1, activation='relu', name='inception_5b_3_3_reduce') inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3,activation='relu', name='inception_5b_3_3') inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48, filter_size=1, activation='relu', name='inception_5b_5_5_reduce') inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce,128, filter_size=5, activation='relu', name='inception_5b_5_5' ) inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool') inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1, activation='relu', name='inception_5b_pool_1_1') inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=3, mode='concat') pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1) pool5_7_7 = dropout(pool5_7_7, 0.4) loss = fully_connected(pool5_7_7, output,activation='softmax') network = regression(loss, optimizer='momentum', loss='categorical_crossentropy', learning_rate=lr, name='targets') model = tflearn.DNN(network, max_checkpoints=0, tensorboard_verbose=0,tensorboard_dir='log') return model
# IMDB Dataset loading train, test, _ = imdb.load_data(path='imdb.pkl', n_words=10000, valid_portion=0.1) trainX, trainY = train testX, testY = test # Data preprocessing # Sequence padding trainX = pad_sequences(trainX, maxlen=100, value=0.) testX = pad_sequences(testX, maxlen=100, value=0.) # Converting labels to binary vectors trainY = to_categorical(trainY) testY = to_categorical(testY) # Building convolutional network network = input_data(shape=[None, 100], name='input') network = tflearn.embedding(network, input_dim=10000, output_dim=128) branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2") branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2") branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2") network = merge([branch1, branch2, branch3], mode='concat', axis=1) network = tf.expand_dims(network, 2) network = global_max_pool(network) network = dropout(network, 0.5) network = fully_connected(network, 2, activation='softmax') network = regression(network, optimizer='adam', learning_rate=0.001, loss='categorical_crossentropy', name='target') # Training model = tflearn.DNN(network, tensorboard_verbose=0) model.fit(trainX, trainY, n_epoch = 5, shuffle=True, validation_set=(testX, testY), show_metric=True, batch_size=32)
pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2) pool1_3_3 = local_response_normalization(pool1_3_3) conv2_3_3_reduce = conv_2d(pool1_3_3, 64, 1, activation='relu', name='conv2_3_3_reduce') conv2_3_3 = conv_2d(conv2_3_3_reduce, 192, 3, activation='relu', name='conv2_3_3') conv2_3_3 = local_response_normalization(conv2_3_3) pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2') # 3a inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1') inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96, 1, activation='relu', name='inception_3a_3_3_reduce') inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128, filter_size=3, activation='relu', name='inception_3a_3_3') inception_3a_5_5_reduce = conv_2d(pool2_3_3, 16, filter_size=1, activation='relu', name='inception_3a_5_5_reduce') inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name='inception_3a_5_5') inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, name='inception_3a_pool') inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1') inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=3) # 3b inception_3b_1_1 = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_1_1') inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce') inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu', name='inception_3b_3_3') inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu', name='inception_3b_5_5_reduce') inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name='inception_3b_5_5') inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool') inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1, activation='relu', name='inception_3b_pool_1_1') inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat', axis=3, name='inception_3b_output') pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3') # 4a inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1') inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce')
def construct_inceptionv1onfire (x,y): # Build network as per architecture in [Dunnings/Breckon, 2018] network = input_data(shape=[None, y, x, 3]) conv1_7_7 = conv_2d(network, 64, 5, strides=2, activation='relu', name = 'conv1_7_7_s2') pool1_3_3 = max_pool_2d(conv1_7_7, 3,strides=2) pool1_3_3 = local_response_normalization(pool1_3_3) conv2_3_3_reduce = conv_2d(pool1_3_3, 64,1, activation='relu',name = 'conv2_3_3_reduce') conv2_3_3 = conv_2d(conv2_3_3_reduce, 128,3, activation='relu', name='conv2_3_3') conv2_3_3 = local_response_normalization(conv2_3_3) pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2') inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1') inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96,1, activation='relu', name='inception_3a_3_3_reduce') inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128,filter_size=3, activation='relu', name = 'inception_3a_3_3') inception_3a_5_5_reduce = conv_2d(pool2_3_3,16, filter_size=1,activation='relu', name ='inception_3a_5_5_reduce' ) inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name= 'inception_3a_5_5') inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, ) inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1') # merge the inception_3a__ inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=3) inception_3b_1_1 = conv_2d(inception_3a_output, 128,filter_size=1,activation='relu', name= 'inception_3b_1_1' ) inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce') inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu',name='inception_3b_3_3') inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu', name = 'inception_3b_5_5_reduce') inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name = 'inception_3b_5_5') inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool') inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1,activation='relu', name='inception_3b_pool_1_1') #merge the inception_3b_* inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat',axis=3,name='inception_3b_output') pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3') inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1') inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce') inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3') inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce') inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5') inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool') inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1') inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=3, name='inception_4a_output') pool5_7_7 = avg_pool_2d(inception_4a_output, kernel_size=5, strides=1) pool5_7_7 = dropout(pool5_7_7, 0.4) loss = fully_connected(pool5_7_7, 2,activation='softmax') network = regression(loss, optimizer='momentum', loss='categorical_crossentropy', learning_rate=0.001) model = tflearn.DNN(network, checkpoint_path='inceptiononv1onfire', max_checkpoints=1, tensorboard_verbose=2) return model