Exemple #1
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def cnn(x):
    with tf.name_scope('cnn') as scope:
        with tf.name_scope('cnn_conv1') as inner_scope:
            wcnn1 = tu.weight([11, 11, 3, 96], name='wcnn1')
            bcnn1 = tu.bias(0.0, [96], name='bcnn1')
            conv1 = tf.add(tu.conv2d(x, wcnn1, stride=(4, 4), padding='SAME'),
                           bcnn1)
            conv1 = tu.relu(conv1)
            norm1 = tu.lrn(conv1,
                           depth_radius=2,
                           bias=1.0,
                           alpha=2e-05,
                           beta=0.75)
            pool1 = tu.max_pool2d(norm1,
                                  kernel=[1, 3, 3, 1],
                                  stride=[1, 2, 2, 1],
                                  padding='VALID')

        with tf.name_scope('cnn_conv2') as inner_scope:
            wcnn2 = tu.weight([5, 5, 96, 256], name='wcnn2')
            bcnn2 = tu.bias(1.0, [256], name='bcnn2')
            conv2 = tf.add(
                tu.conv2d(pool1, wcnn2, stride=(1, 1), padding='SAME'), bcnn2)
            conv2 = tu.relu(conv2)
            norm2 = tu.lrn(conv2,
                           depth_radius=2,
                           bias=1.0,
                           alpha=2e-05,
                           beta=0.75)
            pool2 = tu.max_pool2d(norm2,
                                  kernel=[1, 3, 3, 1],
                                  stride=[1, 2, 2, 1],
                                  padding='VALID')

        with tf.name_scope('cnn_conv3') as inner_scope:
            wcnn3 = tu.weight([3, 3, 256, 384], name='wcnn3')
            bcnn3 = tu.bias(0.0, [384], name='bcnn3')
            conv3 = tf.add(
                tu.conv2d(pool2, wcnn3, stride=(1, 1), padding='SAME'), bcnn3)
            conv3 = tu.relu(conv3)

        with tf.name_scope('cnn_conv4') as inner_scope:
            wcnn4 = tu.weight([3, 3, 384, 384], name='wcnn4')
            bcnn4 = tu.bias(1.0, [384], name='bcnn4')
            conv4 = tf.add(
                tu.conv2d(conv3, wcnn4, stride=(1, 1), padding='SAME'), bcnn4)
            conv4 = tu.relu(conv4)

        with tf.name_scope('cnn_conv5') as inner_scope:
            wcnn5 = tu.weight([3, 3, 384, 256], name='wcnn5')
            bcnn5 = tu.bias(1.0, [256], name='bcnn5')
            conv5 = tf.add(
                tu.conv2d(conv4, wcnn5, stride=(1, 1), padding='SAME'), bcnn5)
            conv5 = tu.relu(conv5)
            pool5 = tu.max_pool2d(conv5,
                                  kernel=[1, 3, 3, 1],
                                  stride=[1, 2, 2, 1],
                                  padding='VALID')

        return pool5
Exemple #2
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def cnn(x):
    """
    AlexNet convolutional layers definition

    Args:
        x: tensor of shape [batch_size, width, height, channels]

    Returns:
        pool5: tensor with all convolutions, pooling and lrn operations applied

    """
    with tf.name_scope('alexnet_cnn') as scope:
        with tf.name_scope('alexnet_cnn_conv1') as inner_scope:
            wcnn1 = tu.weight([11, 11, 3, 96], name='wcnn1')
            bcnn1 = tu.bias(0.0, [96], name='bcnn1')
            conv1 = tf.add(tu.conv2d(x, wcnn1, stride=(4, 4), padding='SAME'), bcnn1)
            # conv1 = tu.batch_norm(conv1)
            conv1 = tu.relu(conv1)
            norm1 = tu.lrn(conv1, depth_radius=2, bias=1.0, alpha=2e-05, beta=0.75)
            pool1 = tu.max_pool2d(norm1, kernel=[1, 3, 3, 1], stride=[1, 2, 2, 1], padding='VALID')

        with tf.name_scope('alexnet_cnn_conv2') as inner_scope:
            wcnn2 = tu.weight([5, 5, 96, 256], name='wcnn2')
            bcnn2 = tu.bias(1.0, [256], name='bcnn2')
            conv2 = tf.add(tu.conv2d(pool1, wcnn2, stride=(1, 1), padding='SAME'), bcnn2)
            # conv2 = tu.batch_norm(conv2)
            conv2 = tu.relu(conv2)
            norm2 = tu.lrn(conv2, depth_radius=2, bias=1.0, alpha=2e-05, beta=0.75)
            pool2 = tu.max_pool2d(norm2, kernel=[1, 3, 3, 1], stride=[1, 2, 2, 1], padding='VALID')

        with tf.name_scope('alexnet_cnn_conv3') as inner_scope:
            wcnn3 = tu.weight([3, 3, 256, 384], name='wcnn3')
            bcnn3 = tu.bias(0.0, [384], name='bcnn3')
            conv3 = tf.add(tu.conv2d(pool2, wcnn3, stride=(1, 1), padding='SAME'), bcnn3)
            # conv3 = tu.batch_norm(conv3)
            conv3 = tu.relu(conv3)

        with tf.name_scope('alexnet_cnn_conv4') as inner_scope:
            wcnn4 = tu.weight([3, 3, 384, 384], name='wcnn4')
            bcnn4 = tu.bias(1.0, [384], name='bcnn4')
            conv4 = tf.add(tu.conv2d(conv3, wcnn4, stride=(1, 1), padding='SAME'), bcnn4)
            # conv4 = tu.batch_norm(conv4)
            conv4 = tu.relu(conv4)

        with tf.name_scope('alexnet_cnn_conv5') as inner_scope:
            wcnn5 = tu.weight([3, 3, 384, 256], name='wcnn5')
            bcnn5 = tu.bias(1.0, [256], name='bcnn5')
            conv5 = tf.add(tu.conv2d(conv4, wcnn5, stride=(1, 1), padding='SAME'), bcnn5)
            # conv5 = tu.batch_norm(conv5)
            conv5 = tu.relu(conv5)
            pool5 = tu.max_pool2d(conv5, kernel=[1, 3, 3, 1], stride=[1, 2, 2, 1], padding='VALID')

        return pool5
Exemple #3
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def cnn(x):
    """
	AlexNet convolutional layers definition

	Args:
		x: tensor of shape [batch_size, width, height, channels]

	Returns:
		pool5: tensor with all convolutions, pooling and lrn operations applied

	"""
    with tf.name_scope('alexnet_cnn') as scope:
        with tf.name_scope('alexnet_cnn_conv1') as inner_scope:
            wcnn1 = tu.weight([11, 11, 3, 96], name='wcnn1')
            # bcnn1 = tu.bias(0.0, [96], name='bcnn1')
            # wcnn1_t = fw(wcnn1)
            # x_t =fa(cabs(x))
            conv1 = tu.conv2d(x, wcnn1, stride=(4, 4), padding='SAME')
            #conv1 = tu.batch_norm(conv1)
            conv1 = tf.nn.relu(conv1)
            norm1 = tu.lrn(conv1,
                           depth_radius=2,
                           bias=1.0,
                           alpha=2e-05,
                           beta=0.75)
            pool1 = tu.max_pool2d(norm1,
                                  kernel=[1, 3, 3, 1],
                                  stride=[1, 2, 2, 1],
                                  padding='VALID')

        with tf.name_scope('alexnet_cnn_conv2') as inner_scope:
            wcnn2 = tu.weight([5, 5, 96, 256], name='wcnn2')
            # bcnn2 = tu.bias(1.0, [256], name='bcnn2')
            pool1_t = fa(cabs(pool1))
            wcnn2_t = fw(wcnn2)
            conv2 = tu.conv2d(pool1_t, wcnn2_t, stride=(1, 1), padding='SAME')
            #conv2 = tu.batch_norm(conv2)
            conv2 = tf.nn.relu(conv2)
            norm2 = tu.lrn(conv2,
                           depth_radius=2,
                           bias=1.0,
                           alpha=2e-05,
                           beta=0.75)
            pool2 = tu.max_pool2d(norm2,
                                  kernel=[1, 3, 3, 1],
                                  stride=[1, 2, 2, 1],
                                  padding='VALID')

        with tf.name_scope('alexnet_cnn_conv3') as inner_scope:
            wcnn3 = tu.weight([3, 3, 256, 384], name='wcnn3')
            # bcnn3 = tu.bias(0.0, [384], name='bcnn3')
            pool2_t = fa(cabs(pool2))
            wcnn3_t = fw(wcnn3)
            conv3 = tu.conv2d(pool2_t, wcnn3_t, stride=(1, 1), padding='SAME')
            #conv3 = tu.batch_norm(conv3)
            conv3 = tf.nn.relu(conv3)

        with tf.name_scope('alexnet_cnn_conv4') as inner_scope:
            wcnn4 = tu.weight([3, 3, 384, 384], name='wcnn4')
            # bcnn4 = tu.bias(1.0, [384], name='bcnn4')
            conv3_t = fa(cabs(conv3))
            wcnn4_t = fw(wcnn4)
            conv4 = tu.conv2d(conv3_t, wcnn4_t, stride=(1, 1), padding='SAME')
            #conv4 = tu.batch_norm(conv4)
            conv4 = tf.nn.relu(conv4)

        with tf.name_scope('alexnet_cnn_conv5') as inner_scope:
            wcnn5 = tu.weight([3, 3, 384, 256], name='wcnn5')
            # bcnn5 = tu.bias(1.0, [256], name='bcnn5')
            conv4_t = fa(cabs(conv4))
            wcnn5_t = fw(wcnn5)
            conv5 = tu.conv2d(conv4_t, wcnn5_t, stride=(1, 1), padding='SAME')
            #conv5 = tu.batch_norm(conv5)
            conv5 = tf.nn.relu(conv5)
            pool5 = tu.max_pool2d(conv5,
                                  kernel=[1, 3, 3, 1],
                                  stride=[1, 2, 2, 1],
                                  padding='VALID')

        return pool5