I'm new in tensorflow and machine learning. Could you explain me how implement deconvulation on this CNN model(http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/) for text classification?CNN model:class TextCNN(object):A CNN for text classification.Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer.def __init__(  self, sequence_length, num_classes, vocab_size,  embedding_size, filter_sizes, num_filters, l2_reg_lambda=0.0):    # Placeholders for input, output and dropout    self.input_x = tf.placeholder(tf.int32, [None, sequence_length], name=input_x)    self.input_y = tf.placeholder(tf.float32, [None, num_classes], name=input_y)    self.dropout_keep_prob = tf.placeholder(tf.float32, name=dropout_keep_prob)    # Keeping track of l2 regularization loss (optional)    l2_loss = tf.constant(0.0)    # Embedding layer    with tf.device('/cpu:0'), tf.name_scope(embedding):        self.W = tf.Variable(            tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0),            name=W)        self.embedded_chars = tf.nn.embedding_lookup(self.W, self.input_x)        self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)    # Create a convolution + maxpool layer for each filter size    pooled_outputs = []    for i, filter_size in enumerate(filter_sizes):        with tf.name_scope(conv-maxpool-%s % filter_size):            # Convolution Layer            filter_shape = [filter_size, embedding_size, 1, num_filters]            W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name=W)            b = tf.Variable(tf.constant(0.1, shape=[num_filters]), name=b)            conv = tf.nn.conv2d(                self.embedded_chars_expanded,                W,                strides=[1, 1, 1, 1],                padding=VALID,                name=conv)            # Apply nonlinearity            h = tf.nn.relu(tf.nn.bias_add(conv, b), name=relu)            # Maxpooling over the outputs            pooled = tf.nn.max_pool(                h,                ksize=[1, sequence_length - filter_size + 1, 1, 1],                strides=[1, 1, 1, 1],                padding='VALID',                name=pool)            pooled_outputs.append(pooled)    # Combine all the pooled features    num_filters_total = num_filters * len(filter_sizes)    self.h_pool = tf.concat(pooled_outputs, 3)    self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])    # Add dropout    with tf.name_scope(dropout):        self.h_drop = tf.nn.dropout(self.h_pool_flat, self.dropout_keep_prob)    # Final (unnormalized) scores and predictions    with tf.name_scope(output):        W = tf.get_variable(            W,            shape=[num_filters_total, num_classes],            initializer=tf.contrib.layers.xavier_initializer())        b = tf.Variable(tf.constant(0.1, shape=[num_classes]), name=b)        l2_loss += tf.nn.l2_loss(W)        l2_loss += tf.nn.l2_loss(b)        self.scores = tf.nn.xw_plus_b(self.h_drop, W, b, name=scores)        self.predictions = tf.argmax(self.scores, 1, name=predictions)    # CalculateMean cross-entropy loss    with tf.name_scope(loss):        losses = tf.nn.softmax_cross_entropy_with_logits(logits=self.scores, labels=self.input_y)        self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss    # Accuracy    with tf.name_scope(accuracy):        correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))        self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, float), name=accuracy)I know about tf.nn.conv2d_transpose, but I don't understand how can I get original data (not embedded_chars)
How can I implement deconvolution on CNN (TensorFlow)?
python;deep learning;tensorflow;convolution
Deconvolution have very simple structure: unpooling → deconvlike this: # UnpoolingPs = (tf.gradients(pooled, h))[0]unpooled = tf.multiply(Ps, P)# Deconvbatch_size = tf.shape(self.input_x)[0]ds = [batch_size]ds.append(self.embedded_chars_expanded.get_shape()[1])ds.append(self.embedded_chars_expanded.get_shape()[2])ds.append(self.embedded_chars_expanded.get_shape()[3])deconv_shape = tf.stack(ds)deconv = tf.nn.conv2d_transpose(    unpooled,    W,    deconv_shape,    strides=[1, 1, 1, 1],    padding='VALID',    name=Deconv    )