Python nnet.conv2d函数代码示例

def _backward_negative_z(inputs, weights, normed_relevances, bias=None):
    inputs_plus = inputs * T.gt(inputs, 0)
    weights_plus = weights * T.gt(weights, 0)
    inputs_minus = inputs * T.lt(inputs, 0)
    weights_minus = weights * T.lt(weights, 0)
    # Compute weights+ * inputs- and weights- * inputs+
    negative_part_a = conv2d(
        normed_relevances, weights_plus.dimshuffle(1, 0, 2, 3)[:, :, ::-1, ::-1], border_mode="full"
    )
    negative_part_a *= inputs_minus
    negative_part_b = conv2d(
        normed_relevances, weights_minus.dimshuffle(1, 0, 2, 3)[:, :, ::-1, ::-1], border_mode="full"
    )
    negative_part_b *= inputs_plus

    together = negative_part_a + negative_part_b
    if bias is not None:
        bias_negative = bias * T.lt(bias, 0)
        bias_relevance = bias_negative.dimshuffle("x", 0, "x", "x") * normed_relevances
        # Divide bias by weight size before convolving back
        # mean across channel, 0, 1 dims (hope this is correct?)
        fraction_bias = bias_relevance / T.prod(weights.shape[1:]).astype(theano.config.floatX)
        bias_rel_in = conv2d(
            fraction_bias, T.ones_like(weights).dimshuffle(1, 0, 2, 3)[:, :, ::-1, ::-1], border_mode="full"
        )
        together += bias_rel_in
    return together

def relevance_conv_z(out_relevances, inputs, weights, bias=None):
    norms_for_relevances = conv2d(inputs, weights)
    if bias is not None:
        norms_for_relevances += bias.dimshuffle("x", 0, "x", "x")
    # stabilize
    # prevent division by 0 and division by small numbers
    eps = 1e-4
    norms_for_relevances += T.sgn(norms_for_relevances) * eps
    norms_for_relevances += T.eq(norms_for_relevances, 0) * eps

    normed_relevances = out_relevances / norms_for_relevances
    # upconv
    in_relevances = conv2d(normed_relevances, weights.dimshuffle(1, 0, 2, 3)[:, :, ::-1, ::-1], border_mode="full")

    in_relevances_proper = in_relevances * inputs

    if bias is not None:
        bias_relevance = bias.dimshuffle("x", 0, "x", "x") * normed_relevances
        # Divide bias by weight size before convolving back
        # mean across channel, 0, 1 dims (hope this is correct?)
        fraction_bias = bias_relevance / T.prod(weights.shape[1:]).astype(theano.config.floatX)
        bias_rel_in = conv2d(
            fraction_bias, T.ones_like(weights).dimshuffle(1, 0, 2, 3)[:, :, ::-1, ::-1], border_mode="full"
        )
        in_relevances_proper += bias_rel_in

    return in_relevances_proper

def convolutional_model(X, w_1, w_2, w_3, w_4, w_5, w_6, p_1, p_2, p_3, p_4, p_5):
    l1 = dropout(T.tanh( max_pool_2d(T.maximum(conv2d(X, w_1, border_mode='full'),0.), (2, 2),ignore_border=True) + b_1.dimshuffle('x', 0, 'x', 'x') ), p_1)
    l2 = dropout(T.tanh( max_pool_2d(T.maximum(conv2d(l1, w_2), 0.), (2, 2),ignore_border=True) + b_2.dimshuffle('x', 0, 'x', 'x') ), p_2)
    l3 = dropout(T.flatten(T.tanh( max_pool_2d(T.maximum(conv2d(l2, w_3), 0.), (2, 2),ignore_border=True) + b_3.dimshuffle('x', 0, 'x', 'x') ), outdim=2), p_3)# flatten to switch back to 1d layers
    l4 = dropout(T.maximum(T.dot(l3, w_4), 0.), p_4)
    l5 = dropout(T.maximum(T.dot(l4, w_5), 0.), p_5)
    return T.dot(l5, w_6)

    def lecun_lcn(self, X, kernel_size=7, threshold = 1e-4, use_divisor=False):
        """
        Yann LeCun's local contrast normalization
        Orginal code in Theano by: Guillaume Desjardins
        """

        filter_shape = (1, 1, kernel_size, kernel_size)
        filters = gaussian_filter(kernel_size).reshape(filter_shape)
        filters = shared(_asarray(filters, dtype=floatX), borrow=True)

        convout = conv2d(X, filters=filters, filter_shape=filter_shape, 
                            border_mode='full')

        # For each pixel, remove mean of kernel_sizexkernel_size neighborhood
        mid = int(floor(kernel_size/2.))
        new_X = X - convout[:,:,mid:-mid,mid:-mid]

        if use_divisor:
            # Scale down norm of kernel_sizexkernel_size patch
            sum_sqr_XX = conv2d(T.sqr(T.abs_(X)), filters=filters, 
                                filter_shape=filter_shape, border_mode='full')

            denom = T.sqrt(sum_sqr_XX[:,:,mid:-mid,mid:-mid])
            per_img_mean = denom.mean(axis=[2,3])
            divisor = T.largest(per_img_mean.dimshuffle(0,1,'x','x'), denom)
            divisor = T.maximum(divisor, threshold)

            new_X /= divisor

        return new_X#T.cast(new_X, floatX)

def relevance_conv_z_plus(out_relevances, inputs, weights, bias=None):
    if bias is not None:
        log.warning("Bias not respected for conv z_plus")
    # hack for negative inputs
    # inputs = T.abs_(inputs)
    weights_plus = weights * T.gt(weights, 0)
    norms_for_relevances = conv2d(inputs, weights_plus)
    # prevent division by 0...
    # adds 1 to every entry that is 0 -> sets 0s to 1
    relevances_are_0 = T.eq(norms_for_relevances, 0)
    norms_for_relevances += relevances_are_0 * 1

    normed_relevances = out_relevances / norms_for_relevances
    # upconv
    in_relevances = conv2d(normed_relevances, weights_plus.dimshuffle(1, 0, 2, 3)[:, :, ::-1, ::-1], border_mode="full")

    in_relevances_proper = in_relevances * inputs

    # Correct for those parts where all inputs of a relevance were
    # zero, spread relevance equally them
    pool_ones = T.ones(weights_plus.shape, dtype=np.float32)
    # mean across channel, 0, 1 dims (hope this is correct?)
    pool_fractions = pool_ones / T.prod(weights_plus.shape[1:]).astype(theano.config.floatX)
    in_relevances_from_0 = conv2d(
        out_relevances * relevances_are_0, pool_fractions.dimshuffle(1, 0, 2, 3), subsample=(1, 1), border_mode="full"
    )

    in_relevances_proper += in_relevances_from_0

    return in_relevances_proper

def theano_kernel_derivative(imshp,kshp,featshp,stride=1):

    features = T.tensor4(dtype=theano.config.floatX)
    kernel = T.tensor4(dtype=theano.config.floatX)
    image = T.tensor4(dtype=theano.config.floatX)

    # Need to transpose first two dimensions of kernel, and reverse index kernel image dims (for correlation)
    kernel_rotated = T.transpose(kernel[:,:,::-1,::-1],axes=[1,0,2,3])

    featshp_logical = (featshp[0],featshp[1],featshp[2]*stride,featshp[3]*stride)
    kshp_rotated = (kshp[1], kshp[0], kshp[2], kshp[3])
    image_estimate = conv2d(features,kernel_rotated,border_mode='full',
                            image_shape=featshp,filter_shape=kshp_rotated,
                            imshp_logical=featshp_logical[1:],kshp_logical=kshp[2:])

    image_error = image - image_estimate

    image_error_rot = T.transpose(image_error,[1,0,2,3])[:,:,::-1,::-1]
    imshp_rot = (imshp[1],imshp[0],imshp[2],imshp[3])
    featshp_rot = (featshp[1],featshp[0],featshp[2],featshp[3])
    features_rot = T.transpose(features,[1,0,2,3])

    featshp_rot_logical = (featshp_rot[0],featshp_rot[1],featshp_rot[2]*stride,featshp_rot[3]*stride)
    kernel_grad_rot = -conv2d(image_error_rot,features_rot,
                              image_shape=imshp_rot,filter_shape=featshp_rot,
                              imshp_logical=imshp_rot[1:],kshp_logical=featshp_rot_logical[2:])
    kernel_grad = T.transpose(kernel_grad_rot,[1,0,2,3])

    return function(inputs=[image,features,kernel],outputs=kernel_grad)

def model(X, params, featMaps, pieces, pDropConv, pDropHidden):
    lnum = 0  # conv: (32, 32) pool: (16, 16)
    layer = conv2d(X, params[lnum][0], border_mode='half') + \
            params[lnum][1].dimshuffle('x', 0, 'x', 'x')
    layer = maxout(layer, featMaps[lnum], pieces[lnum])
    layer = pool_2d(layer, (2, 2), st=(2, 2), ignore_border=False, mode='max')
    layer = basicUtils.dropout(layer, pDropConv)
    lnum += 1  # conv: (16, 16) pool: (8, 8)
    layer = conv2d(layer, params[lnum][0], border_mode='half') + \
            params[lnum][1].dimshuffle('x', 0, 'x', 'x')
    layer = maxout(layer, featMaps[lnum], pieces[lnum])
    layer = pool_2d(layer, (2, 2), st=(2, 2), ignore_border=False, mode='max')
    layer = basicUtils.dropout(layer, pDropConv)
    lnum += 1  # conv: (8, 8) pool: (4, 4)
    layer = conv2d(layer, params[lnum][0], border_mode='half') + \
            params[lnum][1].dimshuffle('x', 0, 'x', 'x')
    layer = maxout(layer, featMaps[lnum], pieces[lnum])
    layer = pool_2d(layer, (2, 2), st=(2, 2), ignore_border=False, mode='max')
    layer = basicUtils.dropout(layer, pDropConv)
    lnum += 1
    layer = T.flatten(layer, outdim=2)
    layer = T.dot(layer, params[lnum][0]) + params[lnum][1].dimshuffle('x', 0)
    layer = relu(layer, alpha=0)
    layer = basicUtils.dropout(layer, pDropHidden)
    lnum += 1
    layer = T.dot(layer, params[lnum][0]) + params[lnum][1].dimshuffle('x', 0)
    layer = relu(layer, alpha=0)
    layer = basicUtils.dropout(layer, pDropHidden)
    lnum += 1
    return softmax(T.dot(layer, params[lnum][0]) + params[lnum][1].dimshuffle('x', 0))  # 如果使用nnet中的softmax训练产生NAN

def relevance_conv_a_b_abs(inputs, weights, out_relevances, a, b, bias=None):
    assert a is not None
    assert b is not None
    assert a - b == 1
    weights_plus = weights * T.gt(weights, 0)
    weights_neg = weights * T.lt(weights, 0)

    plus_norm = conv2d(T.abs_(inputs), weights_plus)
    # stabilize, prevent division by 0
    eps = 1e-4
    plus_norm += T.eq(plus_norm, 0) * eps
    plus_rel_normed = out_relevances / plus_norm
    in_rel_plus = conv2d(plus_rel_normed, weights_plus.dimshuffle(1, 0, 2, 3)[:, :, ::-1, ::-1], border_mode="full")
    in_rel_plus *= T.abs_(inputs)

    # minuses to get positive outputs, since will be subtracted
    # at end of function
    neg_norm = -conv2d(T.abs_(inputs), weights_neg)
    neg_norm += T.eq(neg_norm, 0) * eps
    neg_rel_normed = out_relevances / neg_norm
    in_rel_neg = -conv2d(neg_rel_normed, weights_neg.dimshuffle(1, 0, 2, 3)[:, :, ::-1, ::-1], border_mode="full")
    in_rel_neg *= T.abs_(inputs)

    in_relevance = a * in_rel_plus - b * in_rel_neg
    return in_relevance

def metaOp1(i, j, X, w1, w2, b1, b2):
    # (n,1,r,c)**(16,1,3,3)=(n,16,r,c)
    hiddens = conv2d(X[:, j, :, :, :], w1[i, j, :, :, :, :], border_mode='half') + b1[i, j, :, :, :, :]
    hiddens = relu(hiddens, alpha=0)
    # (n,16,r,c)**(1,16,1,1)=(n,1,r,c)
    outputs = conv2d(hiddens, w2[i, j, :, :, :, :], border_mode='valid') + b2[i, j, :, :, :, :]
    outputs = relu(outputs, alpha=0)
    return outputs

def metaOp(i, j, X, w1, w2, b1, b2):
    # (n,1,r,c)**(16,1,3,3)=(n,16,r,c)
    hiddens = conv2d(X[:, j, :, :, :], w1[i, j, :, :, :, :], border_mode='half') + b1[i, j, :, :, :, :]
    hiddens = relu(hiddens, alpha=0)
    # 在元操作中就需要包含relu激活
    # return conv2d(hiddens, w2[i, j, :, :, :, :], border_mode='valid') + b2[i, j, :, :, :, :]
    # (n,16,r,c)**(1,16,1,1)=(n,1,r,c)
    outputs = conv2d(hiddens, w2[i, j, :, :, :, :], border_mode='valid') + b2[i, j, :, :, :, :]
    return T.nnet.relu(outputs)

	def connect_through(self, alm1_in=None, Pl=None, Ll=None):
		""" connect_through

			@note Note that I made connect_through a separate class method, separate from the automatic initialization, 
			because you can then make changes to the "layer units" or "nodes" before "connecting the layers"
				"""

		if alm1_in is not None:
			self.alm1 = alm1_in

		
		self.Pl = Pl
		alm1 = self.alm1
		c = self.c
		C_lm1,C_l = self.C_ls 
		Wl = self.Wl
		filter_shape = (C_l,C_lm1)+Wl
		assert len(filter_shape) == (2+len(Wl))

		# convolve input feature maps with filters
		if Ll is not None:

			batch_size = alm1.shape[0] # This is m, number of input examples
#			image_shape = (batch_size,C_lm1)+Ll	
			image_shape = (None,C_lm1)+Ll	
			conv_out = conv2d( 
						input=alm1, 
						filters=c, 
						filter_shape=filter_shape,
						input_shape=image_shape)
		else:
			conv_out = conv2d( 
						input=alm1, 
						filters=c, 
						filter_shape=filter_shape)					

		# pool each feature map individually, using maxpooling 
		if Pl is not None:
			pooled_out = pool.pool_2d(
						input=conv_out,
						ws=Pl,
						ignore_border=True)
		
			# add bias term
			if self.psi is None:
				self.al = pooled_out + self.b.dimshuffle('x',0,'x','x')
			else:
				self.al = self.psi( pooled_out + self.b.dimshuffle('x',0,'x','x') )
		else:
			# add bias term
			if self.psi is None:
				self.al = conv_out + self.b.dimshuffle('x',0,'x','x')
			else:
				self.al = self.psi( conv_out + self.b.dimshuffle('x',0,'x','x') )
			

		return self.al

def CNN(x,c_l1,c_l2,f_l1,f_l2):
    conv1=tensor.nnet.relu(conv2d(x,c_l1)) #default stride=1 --subsample=(1,1) 
    pool1=pool_2d(conv1,(2,2),st=(2,2),ignore_border=True)  #default maxpool
    conv2=tensor.nnet.relu(conv2d(pool1,c_l2))
    pool2=pool_2d(conv2,(2,2),st=(2,2),ignore_border=True)
    fpool2=tensor.flatten(pool2,outdim=2)
    full1=tensor.nnet.relu(tensor.dot(fpool2,f_l1))
    pyx=tensor.nnet.sigmoid(tensor.dot(full1,f_l2))

    return c_l1, c_l2, f_l1, f_l2, pyx

def predict_custom_image(params, testImgFilename='own_0.png', activation= activation_convmlp, testImgFilenameDir = '../data/custom/'):

    test_img_value = filter(str.isdigit, testImgFilename)

    test_img = fli.processImg(testImgFilenameDir, testImgFilename)

    nkerns = [20, 50]
    batch_size = 1
    poolsize = (2, 2)

    layer0_input = test_img.reshape((batch_size, 1, 28, 28)).astype(numpy.float32)

    conv_out_0 = conv2d(
        input=layer0_input,
        filters=params[6],
        input_shape=(batch_size, 1, 28, 28),
        filter_shape=(nkerns[0], 1, 5, 5)
    )

    # downsample each feature map individually, using maxpooling
    pooled_out_0 = downsample.max_pool_2d(
        input=conv_out_0,
        ds=poolsize,
        ignore_border=True
    )

    output_0 = activation(pooled_out_0 + params[7].dimshuffle('x', 0, 'x', 'x'))

    conv_out_1 = conv2d(
        input=output_0,
        filters=params[4],
        input_shape=(batch_size, nkerns[0], 12, 12),
        filter_shape=(nkerns[1], nkerns[0], 5, 5),
    )

    # downsample each feature map individually, using maxpooling
    pooled_out_1 = downsample.max_pool_2d(
        input=conv_out_1,
        ds=poolsize,
        ignore_border=True
    )

    output_1 = activation(pooled_out_1 + params[5].dimshuffle('x', 0, 'x', 'x'))
    output_2 = activation(T.dot(output_1.flatten(2), params[2]) + params[3])

    final_output = T.dot(output_2, params[0]) + params[1]
    p_y_given_x = T.nnet.softmax(final_output)
    y_pred = T.argmax(p_y_given_x, axis=1)
    testfunc = theano.function([], [y_pred[0]])
    prediction = testfunc()[0]
    correct = (int(test_img_value) == prediction)
    print('The prediction ' + str(testfunc()[0]) + ' for ' + testImgFilename + '  is ' + str(correct) + '.')
    return correct

def _forward_negative_z(inputs, weights, bias=None):
    inputs_plus = inputs * T.gt(inputs, 0)
    weights_plus = weights * T.gt(weights, 0)
    inputs_minus = inputs * T.lt(inputs, 0)
    weights_minus = weights * T.lt(weights, 0)
    negative_part_a = conv2d(inputs_plus, weights_minus)
    negative_part_b = conv2d(inputs_minus, weights_plus)
    together = negative_part_a + negative_part_b
    if bias is not None:
        bias_negative = bias * T.lt(bias, 0)
        together += bias_negative.dimshuffle("x", 0, "x", "x")

    return together

def conv2d_test_sets():
    def _convert(input, kernel, output, kwargs):
        return [theano.shared(floatX(input)), floatX(kernel), output, kwargs]

    input = np.random.random((3, 1, 16, 16))
    kernel = np.random.random((16, 1, 3, 3))
    output = conv2d(input, kernel).eval()
    yield _convert(input, kernel, output, {})

    input = np.random.random((3, 3, 16, 16))
    kernel = np.random.random((16, 3, 3, 3))
    output = conv2d(input, kernel).eval()
    yield _convert(input, kernel, output, {})