我们从Python开源项目中，提取了以下30个代码示例，用于说明如何使用tflearn.conv_2d()。
def build_dqn(num_actions, action_repeat): """ Building a DQN. """ inputs = tf.placeholder(tf.float32, [None, action_repeat, 84, 84]) # Inputs shape: [batch, channel, height, width] need to be changed into # shape [batch, height, width, channel] net = tf.transpose(inputs, [0, 2, 3, 1]) net = tflearn.conv_2d(net, 32, 8, strides=4, activation='relu') net = tflearn.conv_2d(net, 64, 4, strides=2, activation='relu') net = tflearn.fully_connected(net, 256, activation='relu') q_values = tflearn.fully_connected(net, num_actions) return inputs, q_values # ============================= # ATARI Environment Wrapper # =============================
def resnext(width, height, frame_count, lr, output=9, model_name = 'sentnet_color.model'): net = input_data(shape=[None, width, height, 3], name='input') net = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001) net = tflearn.layers.conv.resnext_block(net, n, 16, 32) net = tflearn.resnext_block(net, 1, 32, 32, downsample=True) net = tflearn.resnext_block(net, n-1, 32, 32) net = tflearn.resnext_block(net, 1, 64, 32, downsample=True) net = tflearn.resnext_block(net, n-1, 64, 32) net = tflearn.batch_normalization(net) net = tflearn.activation(net, 'relu') net = tflearn.global_avg_pool(net) # Regression net = tflearn.fully_connected(net, output, activation='softmax') opt = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True) net = tflearn.regression(net, optimizer=opt, loss='categorical_crossentropy') model = tflearn.DNN(net, max_checkpoints=0, tensorboard_verbose=0, tensorboard_dir='log') return model
def generator(x, reuse=False): with tf.variable_scope('Generator', reuse=reuse): x = tflearn.fully_connected(x, n_units=7 * 7 * 128) x = tflearn.batch_normalization(x) x = tf.nn.tanh(x) x = tf.reshape(x, shape=[-1, 7, 7, 128]) x = tflearn.upsample_2d(x, 2) x = tflearn.conv_2d(x, 64, 5, activation='tanh') x = tflearn.upsample_2d(x, 2) x = tflearn.conv_2d(x, 1, 5, activation='sigmoid') return x # Discriminator
def discriminator(x, reuse=False): with tf.variable_scope('Discriminator', reuse=reuse): x = tflearn.conv_2d(x, 64, 5, activation='tanh') x = tflearn.avg_pool_2d(x, 2) x = tflearn.conv_2d(x, 128, 5, activation='tanh') x = tflearn.avg_pool_2d(x, 2) x = tflearn.fully_connected(x, 1024, activation='tanh') x = tflearn.fully_connected(x, 2) x = tf.nn.softmax(x) return x # Input Data
def vgg16(input, num_class): x = tflearn.conv_2d(input, 64, 3, activation='relu', scope='conv1_1') x = tflearn.conv_2d(x, 64, 3, activation='relu', scope='conv1_2') x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool1') x = tflearn.conv_2d(x, 128, 3, activation='relu', scope='conv2_1') x = tflearn.conv_2d(x, 128, 3, activation='relu', scope='conv2_2') x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool2') x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_1') x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_2') x = tflearn.conv_2d(x, 256, 3, activation='relu', scope='conv3_3') x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool3') x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_1') x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_2') x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv4_3') x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool4') x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_1') x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_2') x = tflearn.conv_2d(x, 512, 3, activation='relu', scope='conv5_3') x = tflearn.max_pool_2d(x, 2, strides=2, name='maxpool5') x = tflearn.fully_connected(x, 4096, activation='relu', scope='fc6') x = tflearn.dropout(x, 0.5, name='dropout1') x = tflearn.fully_connected(x, 4096, activation='relu', scope='fc7') x = tflearn.dropout(x, 0.5, name='dropout2') x = tflearn.fully_connected(x, num_class, activation='softmax', scope='fc8', restore=False) return x
def test_conv_layers(self): X = [[0., 0., 0., 0.], [1., 1., 1., 1.], [0., 0., 1., 0.], [1., 1., 1., 0.]] Y = [[1., 0.], [0., 1.], [1., 0.], [0., 1.]] with tf.Graph().as_default(): g = tflearn.input_data(shape=[None, 4]) g = tflearn.reshape(g, new_shape=[-1, 2, 2, 1]) g = tflearn.conv_2d(g, 4, 2, activation='relu') g = tflearn.max_pool_2d(g, 2) g = tflearn.fully_connected(g, 2, activation='softmax') g = tflearn.regression(g, optimizer='sgd', learning_rate=1.) m = tflearn.DNN(g) m.fit(X, Y, n_epoch=100, snapshot_epoch=False) # TODO: Fix test #self.assertGreater(m.predict([[1., 0., 0., 0.]])[0][0], 0.5) # Bulk Tests with tf.Graph().as_default(): g = tflearn.input_data(shape=[None, 4]) g = tflearn.reshape(g, new_shape=[-1, 2, 2, 1]) g = tflearn.conv_2d(g, 4, 2) g = tflearn.conv_2d(g, 4, 1) g = tflearn.conv_2d_transpose(g, 4, 2, [2, 2]) g = tflearn.max_pool_2d(g, 2)
def sentnet_color_2d(width, height, frame_count, lr, output=9, model_name = 'sentnet_color.model'): network = input_data(shape=[None, width, height, 3], name='input') network = conv_2d(network, 96, 11, strides=4, activation='relu') network = max_pool_2d(network, 3, strides=2) network = local_response_normalization(network) network = conv_2d(network, 256, 5, activation='relu') network = max_pool_2d(network, 3, strides=2) network = local_response_normalization(network) network = conv_2d(network, 384, 3, activation='relu') network = conv_2d(network, 384, 3, activation='relu') network = conv_2d(network, 256, 3, activation='relu') network = max_pool_2d(network, 3, strides=2) network = conv_2d(network, 256, 5, activation='relu') network = max_pool_2d(network, 3, strides=2) network = local_response_normalization(network) network = conv_2d(network, 384, 3, activation='relu') network = conv_2d(network, 384, 3, activation='relu') network = conv_2d(network, 256, 3, activation='relu') network = max_pool_2d(network, 3, strides=2) network = local_response_normalization(network) network = fully_connected(network, 4096, activation='tanh') network = dropout(network, 0.5) network = fully_connected(network, 4096, activation='tanh') network = dropout(network, 0.5) network = fully_connected(network, 4096, activation='tanh') network = dropout(network, 0.5) network = fully_connected(network, 4096, activation='tanh') network = dropout(network, 0.5) network = fully_connected(network, output, activation='softmax') network = regression(network, 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
def alexnet2(width, height, lr, output=3): network = input_data(shape=[None, width, height, 1], name='input') network = conv_2d(network, 96, 11, strides=4, activation='relu') network = max_pool_2d(network, 3, strides=2) network = local_response_normalization(network) network = conv_2d(network, 256, 5, activation='relu') network = max_pool_2d(network, 3, strides=2) network = local_response_normalization(network) network = conv_2d(network, 384, 3, activation='relu') network = conv_2d(network, 384, 3, activation='relu') network = conv_2d(network, 256, 3, activation='relu') network = max_pool_2d(network, 3, strides=2) network = conv_2d(network, 256, 5, activation='relu') network = max_pool_2d(network, 3, strides=2) network = local_response_normalization(network) network = conv_2d(network, 384, 3, activation='relu') network = conv_2d(network, 384, 3, activation='relu') network = conv_2d(network, 256, 3, activation='relu') network = max_pool_2d(network, 3, strides=2) network = local_response_normalization(network) network = fully_connected(network, 4096, activation='tanh') network = dropout(network, 0.5) network = fully_connected(network, 4096, activation='tanh') network = dropout(network, 0.5) network = fully_connected(network, 4096, activation='tanh') network = dropout(network, 0.5) network = fully_connected(network, 4096, activation='tanh') network = dropout(network, 0.5) network = fully_connected(network, output, activation='softmax') network = regression(network, optimizer='momentum', loss='categorical_crossentropy', learning_rate=lr, name='targets') model = tflearn.DNN(network, checkpoint_path='model_alexnet', max_checkpoints=1, tensorboard_verbose=0, tensorboard_dir='log') return model
def alexnet(width, height, lr, output=3): network = input_data(shape=[None, width, height, 1], name='input') network = conv_2d(network, 96, 11, strides=4, activation='relu') network = max_pool_2d(network, 3, strides=2) network = local_response_normalization(network) network = conv_2d(network, 256, 5, activation='relu') network = max_pool_2d(network, 3, strides=2) network = local_response_normalization(network) network = conv_2d(network, 384, 3, activation='relu') network = conv_2d(network, 384, 3, activation='relu') network = conv_2d(network, 256, 3, activation='relu') network = max_pool_2d(network, 3, strides=2) network = local_response_normalization(network) network = fully_connected(network, 4096, activation='tanh') network = dropout(network, 0.5) network = fully_connected(network, 4096, activation='tanh') network = dropout(network, 0.5) network = fully_connected(network, output, activation='softmax') network = regression(network, optimizer='momentum', loss='categorical_crossentropy', learning_rate=lr, name='targets') model = tflearn.DNN(network, checkpoint_path='model_alexnet', max_checkpoints=1, tensorboard_verbose=0, tensorboard_dir='log') return model
def generator(input_image): conv2d = tflearn.conv_2d batch_norm = tflearn.batch_normalization relu = tf.nn.relu ratios = [16, 8, 4, 2, 1] n_filter = 8 net = [] for i in range(len(ratios)): net.append(tflearn.max_pool_2d(input_image, ratios[i], ratios[i])) # block_i_0, block_i_1, block_i_2 for block in range(3): ksize = 1 if (block + 1) % 3 == 0 else 3 net[i] = relu(batch_norm(conv2d(net[i], n_filter, ksize))) if i != 0: # concat with net[i-1] upnet = batch_norm(net[i - 1]) downnet = batch_norm(net[i]) net[i] = tf.concat(3, [upnet, downnet]) # block_i_3, block_i_4, block_i_5 for block in range(3, 6): ksize = 1 if (block + 1) % 3 == 0 else 3 net[i] = conv2d(net[i], n_filter * (i + 1), ksize) net[i] = relu(batch_norm(net[i])) if i != len(ratios) - 1: # upsample for concat net[i] = tflearn.upsample_2d(net[i], 2) nn = len(ratios) - 1 output = conv2d(net[nn], 3, 1) return output
def generator(input_image): relu = tf.nn.relu conv2d = tflearn.conv_2d def batch_norm(x): mean, var = tf.nn.moments(x, axes=[1, 2, 3]) return tf.nn.batch_normalization(x, mean, var, 0, 1, 1e-5) def deconv2d(x, n_filter, ksize, strides=1): _, h, w, _ = x.get_shape().as_list() output_shape = [strides * h, strides * w] return tflearn.conv_2d_transpose(x, n_filter, ksize, output_shape, strides) def res_block(x): net = relu(batch_norm(conv2d(x, 128, 3))) net = batch_norm(conv2d(net, 128, 3)) return x + net net = relu(batch_norm(conv2d(input_image, 32, 9))) net = relu(batch_norm(conv2d(net, 64, 4, strides=2))) net = relu(batch_norm(conv2d(net, 128, 4, strides=2))) for i in range(5): net = res_block(net) net = relu(batch_norm(deconv2d(net, 64, 4, strides=2))) net = relu(batch_norm(deconv2d(net, 32, 4, strides=2))) net = deconv2d(net, 3, 9) return net
def example_net(x): network = tflearn.conv_2d(x, 32, 3, activation='relu') network = tflearn.max_pool_2d(network, 2) network = tflearn.conv_2d(network, 64, 3, activation='relu') network = tflearn.conv_2d(network, 64, 3, activation='relu') network = tflearn.max_pool_2d(network, 2) network = tflearn.fully_connected(network, 512, activation='relu') network = tflearn.dropout(network, 0.5) network = tflearn.fully_connected(network, 3, activation='softmax') return network
def trythisnet(x): network = tflearn.conv_2d(x,64,5,activation='relu') network = tflearn.max_pool_2d(network,3,2) network = tflearn.local_response_normalization(network,4,alpha=0.001/9.0) network = tflearn.conv_2d(network,64,5,activation='relu') network = tflearn.local_response_normalization(network,4,alpha=0.001/9.0) network = tflearn.max_pool_2d(network,3,2) network = tflearn.fully_connected(network,384,activation='relu',weight_decay=0.004) network = tflearn.fully_connected(network,192,activation='relu',weight_decay=0.004) network = tflearn.fully_connected(network,3,activation='softmax',weight_decay=0.0) return network
def mstarnet(x): network = tflearn.conv_2d(x,18,9,activation='relu') network = tflearn.max_pool_2d(network,6) network = tflearn.conv_2d(network,36,5,activation='relu') network = tflearn.max_pool_2d(network,4) network = tflearn.conv_2d(network,120,4,activation='relu') network = tflearn.fully_connected(network,3,activation='softmax') return network
def resnet1(x, classes, n = 5): net = tflearn.conv_2d(x, 16, 3, regularizer='L2', weight_decay=0.0001) net = tflearn.residual_block(net, n, 16) net = tflearn.residual_block(net, 1, 32, downsample=True) net = tflearn.residual_block(net, n - 1, 32) net = tflearn.residual_block(net, 1, 64, downsample=True) net = tflearn.residual_block(net, n - 1, 64) net = tflearn.batch_normalization(net) net = tflearn.activation(net, 'relu') net = tflearn.global_avg_pool(net) # Regression net = tflearn.fully_connected(net, classes, activation='softmax') return net
def run(self): # Real-time pre-processing of the image data img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() # Real-time data augmentation img_aug = tflearn.ImageAugmentation() img_aug.add_random_flip_leftright() # img_aug.add_random_crop([48, 48], padding=8) # Building Residual Network net = tflearn.input_data(shape=[None, 48, 48, 1], data_preprocessing=img_prep, data_augmentation=img_aug) net = tflearn.conv_2d(net, nb_filter=16, filter_size=3, regularizer='L2', weight_decay=0.0001) net = tflearn.residual_block(net, self.n, 16) net = tflearn.residual_block(net, 1, 32, downsample=True) net = tflearn.residual_block(net, self.n - 1, 32) net = tflearn.residual_block(net, 1, 64, downsample=True) net = tflearn.residual_block(net, self.n - 1, 64) net = tflearn.batch_normalization(net) net = tflearn.activation(net, 'relu') net = tflearn.global_avg_pool(net) # Regression net = tflearn.fully_connected(net, 7, activation='softmax') mom = tflearn.Momentum(learning_rate=0.1, lr_decay=0.0001, decay_step=32000, staircase=True, momentum=0.9) net = tflearn.regression(net, optimizer=mom, loss='categorical_crossentropy') self.model = tflearn.DNN(net, checkpoint_path='models/model_resnet_emotion', max_checkpoints=10, tensorboard_verbose=0, clip_gradients=0.) self.model.load('current_model/model_resnet_emotion-42000') face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') cap = cv2.VideoCapture(0) while True: ret, img = cap.read() gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray, 1.3, 5) for (x, y, w, h) in faces: cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2) roi_gray = gray[y:y + h, x:x + w] roi_color = img[y:y + h, x:x + w] self.process_image(roi_gray, img) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows()
def get_model(model_name): # First we load the network print("Setting up neural networks...") n = 18 # Real-time data preprocessing print("Doing preprocessing...") img_prep = tflearn.ImagePreprocessing() img_prep.add_featurewise_zero_center(per_channel=True, mean=[0.573364,0.44924123,0.39455055]) # Real-time data augmentation print("Building augmentation...") img_aug = tflearn.ImageAugmentation() img_aug.add_random_flip_leftright() img_aug.add_random_crop([32, 32], padding=4) #Build the model (for 32 x 32) print("Shaping input data...") net = tflearn.input_data(shape=[None, 32, 32, 3], data_preprocessing=img_prep, data_augmentation=img_aug) net = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001) print("Carving Resnext blocks...") net = tflearn.resnext_block(net, n, 16, 32) net = tflearn.resnext_block(net, 1, 32, 32, downsample=True) net = tflearn.resnext_block(net, n-1, 32, 32) net = tflearn.resnext_block(net, 1, 64, 32, downsample=True) net = tflearn.resnext_block(net, n-1, 64, 32) print("Erroding Gradient...") net = tflearn.batch_normalization(net) net = tflearn.activation(net, 'relu') net = tflearn.global_avg_pool(net) net = tflearn.fully_connected(net, 8, activation='softmax') opt = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True) net = tflearn.regression(net, optimizer=opt, loss='categorical_crossentropy') print("Structuring model...") model = tflearn.DNN(net, tensorboard_verbose=0, clip_gradients=0.) # Load the model from checkpoint print("Loading the model...") model.load(model_name) return model
