The tf.keras.layers.Conv1DTranspose() function is used to apply the transposed 1D convolution operation, also known as deconvolution, on data.
Syntax:tf.keras.layers.Conv1DTranspose( filters, kernel_size, strides=1, padding=’valid’, output_padding=None, data_format=None, dilation_rate=1, activation=None, use_bias=True, kernel_initializer=’glorot_uniform’, bias_initializer=’zeros’, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs)
Input Shape: A 3D tensor of shape: (batch_size, steps, channels)
Output Shape: A 3D tensor of shape: (batch_size, new_steps, filters)
Parameters:
- filters (Integer): The output space’s dimensionality (i.e. the number of output filters in the convolution).
- kernel_size (Integer): The 1D convolution window’s integer length.
- strides: The stride of the convolution along the time dimension.
- padding: The padding mode.
- output_padding:
- data_format: The data format. This specifies the order in which the dimensions in the inputs are ordered. channels_last is the default value.
- dilation_rate: In each dimension, the dilation rate to utilize for the dilated convolution. It should be an integer.
- activation: The layer’s activation function.
- use_bias (Boolean): If the layer has a bias vector or not. True is the default value.
- kernel_initializer: The convolutional kernel weights matrix’s initializer.
- bias_initializer: The bias vector’s initializer.
- kernel_regularizer: The regularizer function applied to the kernel weights matrix.
- bias_regularizer: The regularizer function applied to the bias vector.
- activity_regularizer: The regularizer function applied to the activation.
- kernel_constraint: The constraint for the convolutional kernel weights.
- bias_constraint: The constraint for the bias vector.
Returns: A 3D tensor representing activation(conv1dtranspose(inputs, kernel) + bias).
Example 1:
Python3
import tensorflow as tf tensor_shape=(4, 28, 1) input_shape=tensor_shape[1:] X=tf.random.normal(tensor_shape) def model(input_shape): X_input=tf.keras.layers.Input(shape=input_shape) X_output=tf.keras.layers.Conv1DTranspose(filters=8, kernel_size=4, strides=2)(X_input) model=tf.keras.models.Model(inputs=X_input, outputs=X_output) return model model=model(input_shape) Y=model.predict(X, steps=2) print(Y.shape) |
Output:
(4, 58, 8)
Example 2:
Python3
import tensorflow as tf tensor_shape = (4, 4, 1) input_shape = tensor_shape[1:] X = tf.random.normal(tensor_shape) def model(input_shape): X_input = tf.keras.layers.Input(shape=input_shape) X_output = tf.keras.layers.Conv1DTranspose( filters=3, kernel_size=3, strides=1)(X_input) model = tf.keras.models.Model(inputs=X_input, outputs=X_output) return model model = model(input_shape) Y = model.predict(X, steps=2) print(Y) |
Output:
[[[-0.30124253 -0.36105427 -0.2042067 ] [ 0.02215503 -0.02281483 0.06209912] [ 0.00216722 -0.06402665 -0.45107672] [ 0.61782545 0.6981941 0.5305761 ] [ 0.38394764 0.49401727 -0.32046565] [-0.72445303 -0.70179087 0.51991314]] [[-0.21620852 -0.25913674 -0.14656372] [-0.42101222 -0.5400373 -0.2516055 ] [ 1.1399035 1.2468109 0.51620144] [ 0.45842776 0.60374933 -0.43827266] [-0.996245 -0.97118413 0.717214 ] [ 0.03621851 0.03508553 -0.02599269]] [[-0.23306094 -0.27933523 -0.15798767] [ 0.22609143 0.23278703 0.18968783] [ 0.2541324 0.2872892 -0.21050403] [ 0.47528732 0.6270335 0.680698 ] [ 0.05677184 0.1858277 -0.08888393] [-0.7763872 -0.75210047 0.5571844 ]] [[ 1.2402442 1.4864949 0.8407385 ] [-0.580338 -0.49230838 -0.5872358 ] [-1.7384369 -1.8894652 0.76116455] [ 0.8071178 0.74401593 -0.37187982] [ 0.41134852 0.42184594 -0.30380705] [-0.13865426 -0.13431692 0.09950703]]]
