Python layers.conv() Examples

The following are 5 code examples of layers.conv(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module layers , or try the search function .
Example #1
Source File: model.py    From Img2Img-Translation-Networks with MIT License 6 votes vote down vote up 
def get_scope_and_reuse_conv(network_id):
    """Return the network scope name of conv part given network id.

    We use the ae as name only to make it consistent with pix2pix
    structure but it is not an auto-encoder. For network 1 or
    network 2, the weight is not shared. network 3 shares with network 1
    and network 4 shares with network 2.
    """
    if network_id == 1 or network_id == 2:
        scope = 'ae{}'.format(network_id)
        reuse = False
    elif network_id == 3:
        scope = 'ae1'
        reuse = True
    elif network_id == 4:
        scope = 'ae2'
        reuse = True
    return scope, reuse
Example #2
Source File: cnn.py    From vat_tf with MIT License 5 votes vote down vote up 
def logit(x, is_training=True, update_batch_stats=True, stochastic=True, seed=1234):
    h = x

    rng = numpy.random.RandomState(seed)

    h = L.conv(h, ksize=3, stride=1, f_in=3, f_out=128, seed=rng.randint(123456), name='c1')
    h = L.lrelu(L.bn(h, 128, is_training=is_training, update_batch_stats=update_batch_stats, name='b1'), FLAGS.lrelu_a)
    h = L.conv(h, ksize=3, stride=1, f_in=128, f_out=128, seed=rng.randint(123456), name='c2')
    h = L.lrelu(L.bn(h, 128, is_training=is_training, update_batch_stats=update_batch_stats, name='b2'), FLAGS.lrelu_a)
    h = L.conv(h, ksize=3, stride=1, f_in=128, f_out=128, seed=rng.randint(123456), name='c3')
    h = L.lrelu(L.bn(h, 128, is_training=is_training, update_batch_stats=update_batch_stats, name='b3'), FLAGS.lrelu_a)

    h = L.max_pool(h, ksize=2, stride=2)
    h = tf.nn.dropout(h, keep_prob=FLAGS.keep_prob_hidden, seed=rng.randint(123456)) if stochastic else h

    h = L.conv(h, ksize=3, stride=1, f_in=128, f_out=256, seed=rng.randint(123456), name='c4')
    h = L.lrelu(L.bn(h, 256, is_training=is_training, update_batch_stats=update_batch_stats, name='b4'), FLAGS.lrelu_a)
    h = L.conv(h, ksize=3, stride=1, f_in=256, f_out=256, seed=rng.randint(123456), name='c5')
    h = L.lrelu(L.bn(h, 256, is_training=is_training, update_batch_stats=update_batch_stats, name='b5'), FLAGS.lrelu_a)
    h = L.conv(h, ksize=3, stride=1, f_in=256, f_out=256, seed=rng.randint(123456), name='c6')
    h = L.lrelu(L.bn(h, 256, is_training=is_training, update_batch_stats=update_batch_stats, name='b6'), FLAGS.lrelu_a)

    h = L.max_pool(h, ksize=2, stride=2)
    h = tf.nn.dropout(h, keep_prob=FLAGS.keep_prob_hidden, seed=rng.randint(123456)) if stochastic else h

    h = L.conv(h, ksize=3, stride=1, f_in=256, f_out=512, seed=rng.randint(123456), padding="VALID", name='c7')
    h = L.lrelu(L.bn(h, 512, is_training=is_training, update_batch_stats=update_batch_stats, name='b7'), FLAGS.lrelu_a)
    h = L.conv(h, ksize=1, stride=1, f_in=512, f_out=256, seed=rng.randint(123456), name='c8')
    h = L.lrelu(L.bn(h, 256, is_training=is_training, update_batch_stats=update_batch_stats, name='b8'), FLAGS.lrelu_a)
    h = L.conv(h, ksize=1, stride=1, f_in=256, f_out=128, seed=rng.randint(123456), name='c9')
    h = L.lrelu(L.bn(h, 128, is_training=is_training, update_batch_stats=update_batch_stats, name='b9'), FLAGS.lrelu_a)

    h = tf.reduce_mean(h, reduction_indices=[1, 2])  # Global average pooling
    h = L.fc(h, 128, 10, seed=rng.randint(123456), name='fc')

    if FLAGS.top_bn:
        h = L.bn(h, 10, is_training=is_training,
                 update_batch_stats=update_batch_stats, name='bfc')

    return h
Example #3
Source File: model.py    From QANet_dureader with MIT License 5 votes vote down vote up 
def _fuse(self):

        with tf.variable_scope("Context_to_Query_Attention_Layer"):
            C = tf.tile(tf.expand_dims(self.c_embed_encoding,2),[1,1,self.max_q_len,1])
            Q = tf.tile(tf.expand_dims(self.q_embed_encoding,1),[1,self.max_p_len,1,1])
            S = trilinear([C, Q, C*Q], input_keep_prob = 1.0 - self.dropout)
            mask_q = tf.expand_dims(self.q_mask, 1)
            S_ = tf.nn.softmax(mask_logits(S, mask = mask_q))
            mask_c = tf.expand_dims(self.c_mask, 2)
            S_T = tf.transpose(tf.nn.softmax(mask_logits(S, mask = mask_c), dim = 1),(0,2,1))
            self.c2q = tf.matmul(S_, self.q_embed_encoding)
            self.q2c = tf.matmul(tf.matmul(S_, S_T), self.c_embed_encoding)
            self.attention_outputs = [self.c_embed_encoding, self.c2q, self.c_embed_encoding * self.c2q, self.c_embed_encoding * self.q2c]

        N, PL, QL, CL, d, dc, nh = self._params()
        if self.config.fix_pretrained_vector:
            dc = self.char_mat.get_shape()[-1]
        with tf.variable_scope("Model_Encoder_Layer"):
            inputs = tf.concat(self.attention_outputs, axis = -1)
            self.enc = [conv(inputs, d, name = "input_projection")]
            for i in range(3):
                if i % 2 == 0:
                    self.enc[i] = tf.nn.dropout(self.enc[i], 1.0 - self.dropout)
                self.enc.append(
                    residual_block(self.enc[i],
                        num_blocks = 1,
                        num_conv_layers = 2,
                        kernel_size = 5,
                        mask = self.c_mask,
                        num_filters = d,
                        num_heads = nh,
                        seq_len = self.c_len,
                        scope = "Model_Encoder",
                        bias = False,
                        reuse = True if i > 0 else None,
                        dropout = self.dropout)
                    )

            for i, item in enumerate(self.enc):
                self.enc[i] = tf.reshape(self.enc[i], 
                                    [N, -1, self.enc[i].get_shape()[-1]])
Example #4
Source File: model.py    From QANet_dureader with MIT License 5 votes vote down vote up 
def _decode(self):

        N, PL, QL, CL, d, dc, nh = self._params()

        if self.config.use_position_attn:
            start_logits = tf.squeeze(
                conv(self._attention(tf.concat([self.enc[1], self.enc[2]], axis = -1), name="attn1"), 1, bias = False, name = "start_pointer"), -1)
            end_logits = tf.squeeze(
                conv(self._attention(tf.concat([self.enc[1], self.enc[3]], axis = -1), name="attn2"), 1, bias = False, name = "end_pointer"), -1)
        else:
            start_logits = tf.squeeze(
                conv(tf.concat([self.enc[1], self.enc[2]], axis = -1), 1, bias = False, name = "start_pointer"), -1)
            end_logits = tf.squeeze(
                conv(tf.concat([self.enc[1], self.enc[3]], axis = -1), 1, bias = False, name = "end_pointer"), -1)

        self.logits = [mask_logits(start_logits, mask = tf.reshape(self.c_mask, [N, -1])),
                        mask_logits(end_logits, mask = tf.reshape(self.c_mask, [N, -1]))]

        self.logits1, self.logits2 = [l for l in self.logits]

        outer = tf.matmul(tf.expand_dims(tf.nn.softmax(self.logits1), axis=2),
                              tf.expand_dims(tf.nn.softmax(self.logits2), axis=1))

        outer = tf.matrix_band_part(outer, 0, self.max_a_len)
        self.yp1 = tf.argmax(tf.reduce_max(outer, axis=2), axis=1)
        self.yp2 = tf.argmax(tf.reduce_max(outer, axis=1), axis=1)
Example #5
Source File: cnn.py    From adanet with MIT License 5 votes vote down vote up 
def logit(x, is_training=True, update_batch_stats=True, stochastic=True, seed=1234):
    h = x

    rng = numpy.random.RandomState(seed)

    h = L.conv(h, ksize=3, stride=1, f_in=3, f_out=128, seed=rng.randint(123456), name='c1')
    h = L.lrelu(L.bn(h, 128, is_training=is_training, update_batch_stats=update_batch_stats, name='b1'), FLAGS.lrelu_a)
    h = L.conv(h, ksize=3, stride=1, f_in=128, f_out=128, seed=rng.randint(123456), name='c2')
    h = L.lrelu(L.bn(h, 128, is_training=is_training, update_batch_stats=update_batch_stats, name='b2'), FLAGS.lrelu_a)
    h = L.conv(h, ksize=3, stride=1, f_in=128, f_out=128, seed=rng.randint(123456), name='c3')
    h = L.lrelu(L.bn(h, 128, is_training=is_training, update_batch_stats=update_batch_stats, name='b3'), FLAGS.lrelu_a)

    h = L.max_pool(h, ksize=2, stride=2)
    h = tf.nn.dropout(h, keep_prob=FLAGS.keep_prob_hidden, seed=rng.randint(123456)) if stochastic else h

    h = L.conv(h, ksize=3, stride=1, f_in=128, f_out=256, seed=rng.randint(123456), name='c4')
    h = L.lrelu(L.bn(h, 256, is_training=is_training, update_batch_stats=update_batch_stats, name='b4'), FLAGS.lrelu_a)
    h = L.conv(h, ksize=3, stride=1, f_in=256, f_out=256, seed=rng.randint(123456), name='c5')
    h = L.lrelu(L.bn(h, 256, is_training=is_training, update_batch_stats=update_batch_stats, name='b5'), FLAGS.lrelu_a)
    h = L.conv(h, ksize=3, stride=1, f_in=256, f_out=256, seed=rng.randint(123456), name='c6')
    h = L.lrelu(L.bn(h, 256, is_training=is_training, update_batch_stats=update_batch_stats, name='b6'), FLAGS.lrelu_a)

    h = L.max_pool(h, ksize=2, stride=2)
    h = tf.nn.dropout(h, keep_prob=FLAGS.keep_prob_hidden, seed=rng.randint(123456)) if stochastic else h

    h = L.conv(h, ksize=3, stride=1, f_in=256, f_out=512, seed=rng.randint(123456), padding="VALID", name='c7')
    h = L.lrelu(L.bn(h, 512, is_training=is_training, update_batch_stats=update_batch_stats, name='b7'), FLAGS.lrelu_a)
    h = L.conv(h, ksize=1, stride=1, f_in=512, f_out=256, seed=rng.randint(123456), name='c8')
    h = L.lrelu(L.bn(h, 256, is_training=is_training, update_batch_stats=update_batch_stats, name='b8'), FLAGS.lrelu_a)
    h = L.conv(h, ksize=1, stride=1, f_in=256, f_out=128, seed=rng.randint(123456), name='c9')
    h = L.lrelu(L.bn(h, 128, is_training=is_training, update_batch_stats=update_batch_stats, name='b9'), FLAGS.lrelu_a)

    h1 = tf.reduce_mean(h, reduction_indices=[1, 2])  # Features to be aligned
    h = L.fc(h1, 128, 10, seed=rng.randint(123456), name='fc')

    if FLAGS.top_bn:
        h = L.bn(h, 10, is_training=is_training,
                 update_batch_stats=update_batch_stats, name='bfc')

    return h, h1