我们从Python开源项目中，提取了以下49个代码示例，用于说明如何使用tflearn.activation()。
def sentnet_LSTM_gray(width, height, frame_count, lr, output=9): network = input_data(shape=[None, width, height], name='input') #network = tflearn.input_data(shape=[None, 28, 28], name='input') network = tflearn.lstm(network, 128, return_seq=True) network = tflearn.lstm(network, 128) network = tflearn.fully_connected(network, 9, activation='softmax') network = tflearn.regression(network, optimizer='adam', loss='categorical_crossentropy', name="output1") model = tflearn.DNN(network, checkpoint_path='model_lstm', max_checkpoints=1, tensorboard_verbose=0, tensorboard_dir='log') return model
def test_feed_dict_no_None(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], name="X_in") 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.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) def do_fit(): m.fit({"X_in": X, 'non_existent': X}, Y, n_epoch=30, snapshot_epoch=False) self.assertRaisesRegexp(Exception, "Feed dict asks for variable named 'non_existent' but no such variable is known to exist", do_fit)
def create_critic_network(self): inputs = tflearn.input_data(shape=[None, self.s_dim]) action = tflearn.input_data(shape=[None, self.a_dim]) net = tflearn.fully_connected(inputs, 400, activation='relu') # Add the action tensor in the 2nd hidden layer # Use two temp layers to get the corresponding weights and biases t1 = tflearn.fully_connected(net, 300) t2 = tflearn.fully_connected(action, 300) net = tflearn.activation(tf.matmul(net, t1.W) + tf.matmul(action, t2.W) + t2.b, activation='relu') # linear layer connected to 1 output representing Q(s,a) # Weights are init to Uniform[-3e-3, 3e-3] w_init = tflearn.initializations.uniform(minval=-0.003, maxval=0.003) out = tflearn.fully_connected(net, 1, weights_init=w_init) return inputs, action, out
def create_critic_network(self): inputs = tflearn.input_data(shape=[None, self.s_dim]) action = tflearn.input_data(shape=[None, self.a_dim]) net = tflearn.fully_connected(inputs, 400) net = tflearn.layers.normalization.batch_normalization(net) net = tflearn.activations.relu(net) # Add the action tensor in the 2nd hidden layer # Use two temp layers to get the corresponding weights and biases t1 = tflearn.fully_connected(net, 300) t2 = tflearn.fully_connected(action, 300) net = tflearn.activation( tf.matmul(net, t1.W) + tf.matmul(action, t2.W) + t2.b, activation='relu') # linear layer connected to 1 output representing Q(s,a) # Weights are init to Uniform[-3e-3, 3e-3] w_init = tflearn.initializations.uniform(minval=-0.003, maxval=0.003) out = tflearn.fully_connected(net, 1, weights_init=w_init) return inputs, action, out
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 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 test_recurrent_layers(self): X = [[1, 3, 5, 7], [2, 4, 8, 10], [1, 5, 9, 11], [2, 6, 8, 0]] Y = [[0., 1.], [1., 0.], [0., 1.], [1., 0.]] with tf.Graph().as_default(): g = tflearn.input_data(shape=[None, 4]) g = tflearn.embedding(g, input_dim=12, output_dim=4) g = tflearn.lstm(g, 6) 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=300, snapshot_epoch=False) self.assertGreater(m.predict([[5, 9, 11, 1]])[0][1], 0.9)
def test_regression_placeholder(self): ''' Check that regression does not duplicate placeholders ''' with tf.Graph().as_default(): g = tflearn.input_data(shape=[None, 2]) g_nand = tflearn.fully_connected(g, 1, activation='linear') with tf.name_scope("Y"): Y_in = tf.placeholder(shape=[None, 1], dtype=tf.float32, name="Y") tflearn.regression(g_nand, optimizer='sgd', placeholder=Y_in, learning_rate=2., loss='binary_crossentropy', op_name="regression1", name="Y") # for this test, just use the same default trainable_vars # in practice, this should be different for the two regressions tflearn.regression(g_nand, optimizer='adam', placeholder=Y_in, learning_rate=2., loss='binary_crossentropy', op_name="regression2", name="Y") self.assertEqual(len(tf.get_collection(tf.GraphKeys.TARGETS)), 1)
def create_actor_network(self): inputs = tflearn.input_data(shape=[None, self.s_dim]) net = tflearn.fully_connected(inputs, 400, activation='relu') net = tflearn.fully_connected(net, 300, activation='relu') # Final layer weights are init to Uniform[-3e-3, 3e-3] w_init = tflearn.initializations.uniform(minval=-0.003, maxval=0.003) out = tflearn.fully_connected(net, self.a_dim, activation='tanh', weights_init=w_init) scaled_out = tf.multiply(out, self.action_bound) # Scale output to -action_bound to action_bound return inputs, out, scaled_out
def create_actor_network(self): inputs = tflearn.input_data(shape=[None, self.s_dim]) net = tflearn.fully_connected(inputs, 400) net = tflearn.layers.normalization.batch_normalization(net) net = tflearn.activations.relu(net) net = tflearn.fully_connected(net, 300) net = tflearn.layers.normalization.batch_normalization(net) net = tflearn.activations.relu(net) # Final layer weights are init to Uniform[-3e-3, 3e-3] w_init = tflearn.initializations.uniform(minval=-0.003, maxval=0.003) out = tflearn.fully_connected( net, self.a_dim, activation='tanh', weights_init=w_init) # Scale output to -action_bound to action_bound scaled_out = tf.multiply(out, self.action_bound) return inputs, out, scaled_out
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 sentnet_color(width, height, frame_count, lr, output=9, model_name = 'sentnet_color.model'): network = input_data(shape=[None, width, height,3, 1], name='input') network = conv_3d(network, 96, 11, strides=4, activation='relu') network = max_pool_3d(network, 3, strides=2) #network = local_response_normalization(network) network = conv_3d(network, 256, 5, activation='relu') network = max_pool_3d(network, 3, strides=2) #network = local_response_normalization(network) network = conv_3d(network, 384, 3, activation='relu') network = conv_3d(network, 384, 3, activation='relu') network = conv_3d(network, 256, 3, activation='relu') network = max_pool_3d(network, 3, strides=2) network = conv_3d(network, 256, 5, activation='relu') network = max_pool_3d(network, 3, strides=2) #network = local_response_normalization(network) network = conv_3d(network, 384, 3, activation='relu') network = conv_3d(network, 384, 3, activation='relu') network = conv_3d(network, 256, 3, activation='relu') network = max_pool_3d(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_name, max_checkpoints=1, tensorboard_verbose=0, tensorboard_dir='log') return model
def sentnet_frames(width, height, frame_count, lr, output=9): network = input_data(shape=[None, width, height,frame_count, 1], name='input') network = conv_3d(network, 96, 11, strides=4, activation='relu') network = max_pool_3d(network, 3, strides=2) #network = local_response_normalization(network) network = conv_3d(network, 256, 5, activation='relu') network = max_pool_3d(network, 3, strides=2) #network = local_response_normalization(network) network = conv_3d(network, 384, 3, activation='relu') network = conv_3d(network, 384, 3, activation='relu') network = conv_3d(network, 256, 3, activation='relu') network = max_pool_3d(network, 3, strides=2) network = conv_3d(network, 256, 5, activation='relu') network = max_pool_3d(network, 3, strides=2) #network = local_response_normalization(network) network = conv_3d(network, 384, 3, activation='relu') network = conv_3d(network, 384, 3, activation='relu') network = conv_3d(network, 256, 3, activation='relu') network = max_pool_3d(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 sentnet(width, height, frame_count, lr, output=9): network = input_data(shape=[None, width, height, frame_count, 1], name='input') network = conv_3d(network, 96, 11, strides=4, activation='relu') network = avg_pool_3d(network, 3, strides=2) #network = local_response_normalization(network) network = conv_3d(network, 256, 5, activation='relu') network = avg_pool_3d(network, 3, strides=2) #network = local_response_normalization(network) network = conv_3d(network, 384, 3, activation='relu') network = conv_3d(network, 384, 3, activation='relu') network = conv_3d(network, 256, 3, activation='relu') network = max_pool_3d(network, 3, strides=2) network = conv_3d(network, 256, 5, activation='relu') network = avg_pool_3d(network, 3, strides=2) #network = local_response_normalization(network) network = conv_3d(network, 384, 3, activation='relu') network = conv_3d(network, 384, 3, activation='relu') network = conv_3d(network, 256, 3, activation='relu') network = avg_pool_3d(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 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 sentnet_v0(width, height, frame_count, lr, output=9): network = input_data(shape=[None, width, height, frame_count, 1], name='input') network = conv_3d(network, 96, 11, strides=4, activation='relu') network = max_pool_3d(network, 3, strides=2) #network = local_response_normalization(network) network = conv_3d(network, 256, 5, activation='relu') network = max_pool_3d(network, 3, strides=2) #network = local_response_normalization(network) network = conv_3d(network, 384, 3, 3, activation='relu') network = conv_3d(network, 384, 3, 3, activation='relu') network = conv_3d(network, 256, 3, 3, activation='relu') network = max_pool_3d(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 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 sentnet2(width, height, frame_count, lr, output=9): network = input_data(shape=[None, width, height, frame_count, 1], name='input') network = conv_3d(network, 96, 11, strides=4, activation='relu') network = max_pool_3d(network, 3, strides=2) #network = local_response_normalization(network) network = conv_3d(network, 256, 5, activation='relu') network = max_pool_3d(network, 3, strides=2) #network = local_response_normalization(network) network = conv_3d(network, 384, 3, activation='relu') network = conv_3d(network, 384, 3, activation='relu') network = conv_3d(network, 256, 3, activation='relu') network = max_pool_3d(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, 3, 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 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 test_core_layers(self): X = [[0., 0.], [0., 1.], [1., 0.], [1., 1.]] Y_nand = [[1.], [1.], [1.], [0.]] Y_or = [[0.], [1.], [1.], [1.]] # Graph definition with tf.Graph().as_default(): # Building a network with 2 optimizers g = tflearn.input_data(shape=[None, 2]) # Nand operator definition g_nand = tflearn.fully_connected(g, 32, activation='linear') g_nand = tflearn.fully_connected(g_nand, 32, activation='linear') g_nand = tflearn.fully_connected(g_nand, 1, activation='sigmoid') g_nand = tflearn.regression(g_nand, optimizer='sgd', learning_rate=2., loss='binary_crossentropy') # Or operator definition g_or = tflearn.fully_connected(g, 32, activation='linear') g_or = tflearn.fully_connected(g_or, 32, activation='linear') g_or = tflearn.fully_connected(g_or, 1, activation='sigmoid') g_or = tflearn.regression(g_or, optimizer='sgd', learning_rate=2., loss='binary_crossentropy') # XOR merging Nand and Or operators g_xor = tflearn.merge([g_nand, g_or], mode='elemwise_mul') # Training m = tflearn.DNN(g_xor) m.fit(X, [Y_nand, Y_or], n_epoch=400, snapshot_epoch=False) # Testing self.assertLess(m.predict([[0., 0.]])[0][0], 0.01) self.assertGreater(m.predict([[0., 1.]])[0][0], 0.9) self.assertGreater(m.predict([[1., 0.]])[0][0], 0.9) self.assertLess(m.predict([[1., 1.]])[0][0], 0.01) # Bulk Tests with tf.Graph().as_default(): net = tflearn.input_data(shape=[None, 2]) net = tflearn.flatten(net) net = tflearn.reshape(net, new_shape=[-1]) net = tflearn.activation(net, 'relu') net = tflearn.dropout(net, 0.5) net = tflearn.single_unit(net)
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
