Python tflearn.dropout函数代码示例

def use_tflearn():
    import tflearn

    # Data loading and preprocessing
    import tflearn.datasets.mnist as mnist
    X, Y, testX, testY = mnist.load_data(one_hot=True)

    # Building deep neural network
    input_layer = tflearn.input_data(shape=[None, 784])
    dense1 = tflearn.fully_connected(input_layer, 64, activation='tanh',
                                     regularizer='L2', weight_decay=0.001)
    dropout1 = tflearn.dropout(dense1, 0.8)
    dense2 = tflearn.fully_connected(dropout1, 64, activation='tanh',
                                     regularizer='L2', weight_decay=0.001)
    dropout2 = tflearn.dropout(dense2, 0.8)
    softmax = tflearn.fully_connected(dropout2, 10, activation='softmax')

    # Regression using SGD with learning rate decay and Top-3 accuracy
    sgd = tflearn.SGD(learning_rate=0.1, lr_decay=0.96, decay_step=1000)
    top_k = tflearn.metrics.Top_k(3)
    net = tflearn.regression(softmax, optimizer=sgd, metric=top_k,
                             loss='categorical_crossentropy')

    # Training
    model = tflearn.DNN(net, tensorboard_verbose=0)
    model.fit(X, Y, n_epoch=20, validation_set=(testX, testY),
              show_metric=True, run_id="dense_model")

def run():
    # imagine cnn, the third dim is like the 'chnl'
    g = tflearn.input_data(shape=[None, maxlen, len(char_idx)])
    g = tflearn.lstm(g, 512, return_seq=True)
    g = tflearn.dropout(g, 0.5)
    g = tflearn.lstm(g, 512)
    g = tflearn.dropout(g, 0.5)
    g = tflearn.fully_connected(g, len(char_idx), activation='softmax')
    g = tflearn.regression(g, optimizer='adam',
                           loss='categorical_crossentropy',
                           learning_rate=0.001)

    m = tflearn.SequenceGenerator(g, dictionary=char_idx,
                                  seq_maxlen=maxlen,
                                  clip_gradients=5.0,
                                  checkpoint_path='models/model_us_cities')

    for i in range(40):
        seed = random_sequence_from_textfile(path, maxlen)
        m.fit(X, Y, validation_set=0.1, batch_size=128,
              n_epoch=1, run_id='us_cities')
        print("-- TESTING...")
        print("-- Test with temperature of 1.2 --")
        print(m.generate(30, temperature=1.2, seq_seed=seed))
        print("-- Test with temperature of 1.0 --")
        print(m.generate(30, temperature=1.0, seq_seed=seed))
        print("-- Test with temperature of 0.5 --")
        print(m.generate(30, temperature=0.5, seq_seed=seed))

def yn_net():
    net = tflearn.input_data(shape=[None, img_rows, img_cols, 1]) #D = 256, 256
    net = tflearn.conv_2d(net,nb_filter=8,filter_size=3, activation='relu', name='conv0.1')
    net = tflearn.conv_2d(net,nb_filter=8,filter_size=3, activation='relu', name='conv0.2')
    net = tflearn.max_pool_2d(net, kernel_size = [2,2], name='maxpool0') #D = 128, 128
    net = tflearn.dropout(net,0.75,name='dropout0')
    net = tflearn.conv_2d(net,nb_filter=16,filter_size=3, activation='relu', name='conv1.1')
    net = tflearn.conv_2d(net,nb_filter=16,filter_size=3, activation='relu', name='conv1.2')
    net = tflearn.max_pool_2d(net, kernel_size = [2,2], name='maxpool1') #D = 64,  64
    net = tflearn.dropout(net,0.75,name='dropout0')
    net = tflearn.conv_2d(net,nb_filter=32,filter_size=3, activation='relu', name='conv2.1')
    net = tflearn.conv_2d(net,nb_filter=32,filter_size=3, activation='relu', name='conv2.2')
    net = tflearn.max_pool_2d(net, kernel_size = [2,2], name='maxpool2') #D = 32 by 32
    net = tflearn.dropout(net,0.75,name='dropout0')
    net = tflearn.conv_2d(net,nb_filter=32,filter_size=3, activation='relu', name='conv3.1')
    net = tflearn.conv_2d(net,nb_filter=32,filter_size=3, activation='relu', name='conv3.2')
    net = tflearn.max_pool_2d(net, kernel_size = [2,2], name='maxpool3') #D = 16 by 16
    net = tflearn.dropout(net,0.75,name='dropout0')
#    net = tflearn.conv_2d(net,nb_filter=64,filter_size=3, activation='relu', name='conv4.1')
#    net = tflearn.conv_2d(net,nb_filter=64,filter_size=3, activation='relu', name='conv4.2')
#    net = tflearn.max_pool_2d(net, kernel_size = [2,2], name='maxpool4') #D = 8 by 8
#    net = tflearn.dropout(net,0.75,name='dropout0')
    net = tflearn.fully_connected(net, n_units = 128, activation='relu', name='fc1')
    net = tflearn.fully_connected(net, 2, activation='sigmoid')
    net = tflearn.regression(net, optimizer='adam', learning_rate=0.001)
    model = tflearn.DNN(net, tensorboard_verbose=1,tensorboard_dir='/tmp/tflearn_logs/')
    return model

def vgg16(placeholderX=None):

    x = tflearn.input_data(shape=[None, 224, 224, 3], name='input',
                           placeholder=placeholderX)

    x = tflearn.conv_2d(x, 64, 3, activation='relu', name='conv1_1')
    x = tflearn.conv_2d(x, 64, 3, activation='relu', name='conv1_2')
    x = tflearn.max_pool_2d(x, 2, strides=2, name='pool1')

    x = tflearn.conv_2d(x, 128, 3, activation='relu', name='conv2_1')
    x = tflearn.conv_2d(x, 128, 3, activation='relu', name='conv2_2')
    x = tflearn.max_pool_2d(x, 2, strides=2, name='pool2')

    x = tflearn.conv_2d(x, 256, 3, activation='relu', name='conv3_1')
    x = tflearn.conv_2d(x, 256, 3, activation='relu', name='conv3_2')
    x = tflearn.conv_2d(x, 256, 3, activation='relu', name='conv3_3')
    x = tflearn.max_pool_2d(x, 2, strides=2, name='pool3')

    x = tflearn.conv_2d(x, 512, 3, activation='relu', name='conv4_1')
    x = tflearn.conv_2d(x, 512, 3, activation='relu', name='conv4_2')
    x = tflearn.conv_2d(x, 512, 3, activation='relu', name='conv4_3')
    x = tflearn.max_pool_2d(x, 2, strides=2, name='pool4')

    x = tflearn.conv_2d(x, 512, 3, activation='relu', name='conv5_1')
    x = tflearn.conv_2d(x, 512, 3, activation='relu', name='conv5_2')
    x = tflearn.conv_2d(x, 512, 3, activation='relu', name='conv5_3')
    x = tflearn.max_pool_2d(x, 2, strides=2, name='pool5')

    x = tflearn.conv_2d(x, 4096, 7, activation='relu', name='fc6')
    x = tflearn.dropout(x, 0.5)

    x = tflearn.conv_2d(x, 4096, 1, activation='relu', name='fc7')
    x = tflearn.dropout(x, 0.5)

    return x

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 make_core_network(network):
     dense1 = tflearn.fully_connected(network, 64, activation='tanh',
                                      regularizer='L2', weight_decay=0.001, name="dense1")
     dropout1 = tflearn.dropout(dense1, 0.8)
     dense2 = tflearn.fully_connected(dropout1, 64, activation='tanh',
                                      regularizer='L2', weight_decay=0.001, name="dense2")
     dropout2 = tflearn.dropout(dense2, 0.8)
     softmax = tflearn.fully_connected(dropout2, 10, activation='softmax', name="softmax")
     return softmax

def get_model_action():
    # Network building
    net = tflearn.input_data(shape=[None, 10, 128], name='net2_layer1')
    net = tflearn.lstm(net, n_units=256, return_seq=True, name='net2_layer2')
    net = tflearn.dropout(net, 0.6, name='net2_layer3')
    net = tflearn.lstm(net, n_units=256, return_seq=False, name='net2_layer4')
    net = tflearn.dropout(net, 0.6, name='net2_layer5')
    net = tflearn.fully_connected(net, 5, activation='softmax', name='net2_layer6')
    net = tflearn.regression(net, optimizer='sgd', loss='categorical_crossentropy', learning_rate=0.001,
                             name='net2_layer7')
    return tflearn.DNN(net, clip_gradients=5.0, tensorboard_verbose=0)

def shakespeare():


    path = "shakespeare_input.txt"
    #path = "shakespeare_input-100.txt"
    char_idx_file = 'char_idx.pickle'

    if not os.path.isfile(path):
        urllib.request.urlretrieve(
            "https://raw.githubusercontent.com/tflearn/tflearn.github.io/master/resources/shakespeare_input.txt", path)

    maxlen = 25

    char_idx = None
    if os.path.isfile(char_idx_file):
        print('Loading previous char_idx')
        char_idx = pickle.load(open(char_idx_file, 'rb'))

    X, Y, char_idx = \
        textfile_to_semi_redundant_sequences(path, seq_maxlen=maxlen, redun_step=3,
                                             pre_defined_char_idx=char_idx)

    pickle.dump(char_idx, open(char_idx_file, 'wb'))

    g = tflearn.input_data([None, maxlen, len(char_idx)])
    g = tflearn.lstm(g, 512, return_seq=True)
    g = tflearn.dropout(g, 0.5)
    g = tflearn.lstm(g, 512, return_seq=True)
    g = tflearn.dropout(g, 0.5)
    g = tflearn.lstm(g, 512)
    g = tflearn.dropout(g, 0.5)
    g = tflearn.fully_connected(g, len(char_idx), activation='softmax')
    g = tflearn.regression(g, optimizer='adam', loss='categorical_crossentropy',
                           learning_rate=0.001)

    m = tflearn.SequenceGenerator(g, dictionary=char_idx,
                                  seq_maxlen=maxlen,
                                  clip_gradients=5.0,
                                  checkpoint_path='model_shakespeare')

    for i in range(50):
        seed = random_sequence_from_textfile(path, maxlen)
        m.fit(X, Y, validation_set=0.1, batch_size=128,
              n_epoch=1, run_id='shakespeare')
        print("-- TESTING...")
        print("-- Test with temperature of 1.0 --")
        print(m.generate(600, temperature=1.0, seq_seed=seed))
        #print(m.generate(10, temperature=1.0, seq_seed=seed))
        print("-- Test with temperature of 0.5 --")
        print(m.generate(600, temperature=0.5, seq_seed=seed))

    def deep_model(self, wide_inputs, n_inputs, n_nodes=[100, 50], use_dropout=False):
        '''
        Model - deep, i.e. two-layer fully connected network model
        '''
        cc_input_var = {}
        cc_embed_var = {}
        flat_vars = []
        if self.verbose:
            print ("--> deep model: %s categories, %d continuous" % (len(self.categorical_columns), n_inputs))
        for cc, cc_size in self.categorical_columns.items():
            cc_input_var[cc] = tflearn.input_data(shape=[None, 1], name="%s_in" % cc,  dtype=tf.int32)
            # embedding layers only work on CPU!  No GPU implementation in tensorflow, yet!
            cc_embed_var[cc] = tflearn.layers.embedding_ops.embedding(cc_input_var[cc],    cc_size,  8, name="deep_%s_embed" % cc)
            if self.verbose:
                print ("    %s_embed = %s" % (cc, cc_embed_var[cc]))
            flat_vars.append(tf.squeeze(cc_embed_var[cc], squeeze_dims=[1], name="%s_squeeze" % cc))

        network = tf.concat(1, [wide_inputs] + flat_vars, name="deep_concat")
        for k in range(len(n_nodes)):
            network = tflearn.fully_connected(network, n_nodes[k], activation="relu", name="deep_fc%d" % (k+1))
            if use_dropout:
                network = tflearn.dropout(network, 0.5, name="deep_dropout%d" % (k+1))
        if self.verbose:
            print ("Deep model network before output %s" % network)
        network = tflearn.fully_connected(network, 1, activation="linear", name="deep_fc_output", bias=False)
        network = tf.reshape(network, [-1, 1])	# so that accuracy is binary_accuracy
        if self.verbose:
            print ("Deep model network %s" % network)
        return network

    def test_sequencegenerator(self):

        with tf.Graph().as_default():
            text = "123456789101234567891012345678910123456789101234567891012345678910"
            maxlen = 5

            X, Y, char_idx = \
                tflearn.data_utils.string_to_semi_redundant_sequences(text, seq_maxlen=maxlen, redun_step=3)

            g = tflearn.input_data(shape=[None, maxlen, len(char_idx)])
            g = tflearn.lstm(g, 32)
            g = tflearn.dropout(g, 0.5)
            g = tflearn.fully_connected(g, len(char_idx), activation='softmax')
            g = tflearn.regression(g, optimizer='adam', loss='categorical_crossentropy',
                                   learning_rate=0.1)

            m = tflearn.SequenceGenerator(g, dictionary=char_idx,
                                          seq_maxlen=maxlen,
                                          clip_gradients=5.0)
            m.fit(X, Y, validation_set=0.1, n_epoch=100, snapshot_epoch=False)
            res = m.generate(10, temperature=1., seq_seed="12345")
            self.assertEqual(res, "123456789101234", "SequenceGenerator test failed! Generated sequence: " + res + " expected '123456789101234'")

            # Testing save method
            m.save("test_seqgen.tflearn")
            self.assertTrue(os.path.exists("test_seqgen.tflearn"))

            # Testing load method
            m.load("test_seqgen.tflearn")
            res = m.generate(10, temperature=1., seq_seed="12345")
            self.assertEqual(res, "123456789101234", "SequenceGenerator test failed after loading model! Generated sequence: " + res + " expected '123456789101234'")

    def build_cnn_network(self, network):
        """ Build CNN network.

        Args:
            network: base network.

        Returns:
            model: CNN model.

        """
        print('Building CNN network.')
        # Convolutional network building
        network = tflearn.conv_2d(network, 32,
                            self.IMAGE_CHANNEL_NUM,
                          activation='relu')
        network = tflearn.max_pool_2d(network, 2)
        network = tflearn.conv_2d(network, 64,
                          self.IMAGE_CHANNEL_NUM,
                          activation='relu')
        network = tflearn.conv_2d(network, 64,
                          self.IMAGE_CHANNEL_NUM,
                          activation='relu')
        network = tflearn.max_pool_2d(network, 2)
        network = tflearn.fully_connected(
            network, 32 * 32, activation='relu')
        network = tflearn.dropout(network, 0.5)
        # Two category. positive or negative.
        network = tflearn.fully_connected(network, 2,
                                  activation='softmax')
        network = tflearn.regression(network, optimizer='adam',
                             loss='categorical_crossentropy',
                             learning_rate=0.001)
        print("CNN network built.")
        return network

def generator_xss():
    global char_idx
    global xss_data_file
    global maxlen


    if os.path.isfile(char_idx_file):
        print('Loading previous xxs_char_idx')
        char_idx = pickle.load(open(char_idx_file, 'rb'))


    X, Y, char_idx = \
        textfile_to_semi_redundant_sequences(xss_data_file, seq_maxlen=maxlen, redun_step=3,
                                             pre_defined_char_idx=char_idx)


    #pickle.dump(char_idx, open(char_idx_file, 'wb'))

    g = tflearn.input_data([None, maxlen, len(char_idx)])
    g = tflearn.lstm(g, 32, return_seq=True)
    g = tflearn.dropout(g, 0.1)
    g = tflearn.lstm(g, 32, return_seq=True)
    g = tflearn.dropout(g, 0.1)
    g = tflearn.lstm(g, 32)
    g = tflearn.dropout(g, 0.1)
    g = tflearn.fully_connected(g, len(char_idx), activation='softmax')
    g = tflearn.regression(g, optimizer='adam', loss='categorical_crossentropy',
                           learning_rate=0.001)

    m = tflearn.SequenceGenerator(g, dictionary=char_idx,
                                  seq_maxlen=maxlen,
                                  clip_gradients=5.0,
                                  checkpoint_path='chkpoint/model_scanner_poc')

    print "random_sequence_from_textfile"
    #seed = random_sequence_from_textfile(xss_data_file, maxlen)
    seed='"/><script>'
    m.fit(X, Y, validation_set=0.1, batch_size=128,
              n_epoch=2, run_id='scanner-poc')
    print("-- TESTING...")

    print("-- Test with temperature of 0.1 --")
    print(m.generate(32, temperature=0.1, seq_seed=seed))
    print("-- Test with temperature of 0.5 --")
    print(m.generate(32, temperature=0.5, seq_seed=seed))
    print("-- Test with temperature of 1.0 --")
    print(m.generate(32, temperature=1.0, seq_seed=seed))

 def simple_learn(self):
     tflearn.init_graph()
     net=tflearn.input_data(shape=[None,64,64,3])
     net=tflearn.fully_connected(net,64)
     net=tflearn.dropout(net,.5)
     net=tflearn.fully_connected(net,10,activation='softmax')
     net=tflearn.regression(net,optimizer='adam',loss='softmax_categorical_crossentropy')
     model = tflearn.DNN(net)
     model.fit(self.trainset,self.trainlabels)

def generate_net(embedding):
    net = tflearn.input_data([None, 200])
    net = tflearn.embedding(net, input_dim=300000, output_dim=128)
    net = tflearn.lstm(net, 128)
    net = tflearn.dropout(net, 0.5)
    net = tflearn.fully_connected(net, 2, activation='softmax')
    net = tflearn.regression(net, optimizer='adam',
                             loss='categorical_crossentropy')
    return net

def build(embedding_size=(400000, 50), train_embedding=False, hidden_dims=128,
          learning_rate=0.001):
    net = tflearn.input_data([None, 200])
    net = tflearn.embedding(net, input_dim=embedding_size[0],
                            output_dim=embedding_size[1],
                            trainable=train_embedding, name='EmbeddingLayer')
    net = tflearn.lstm(net, hidden_dims, return_seq=True)
    net = tflearn.dropout(net, 0.5)
    net = tflearn.lstm(net, hidden_dims, return_seq=True)
    net = tflearn.dropout(net, 0.5)
    net = tflearn.lstm(net, hidden_dims, return_seq=True)
    net = tflearn.dropout(net, 0.5)
    net = tflearn.lstm(net, hidden_dims)
    net = tflearn.dropout(net, 0.5)
    net = tflearn.fully_connected(net, 2, activation='softmax')
    net = tflearn.regression(net, optimizer='adam', learning_rate=learning_rate,
                             loss='categorical_crossentropy')
    return net