garage.tf.models.cnn module¶
CNN in TensorFlow.
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cnn
(input_var, filters, strides, name, padding, hidden_nonlinearity=<function relu>, hidden_w_init=<tensorflow.python.ops.init_ops_v2.GlorotUniform object>, hidden_b_init=<tensorflow.python.ops.init_ops_v2.Zeros object>)[source]¶ Convolutional neural network (CNN).
Note
Based on ‘NHWC’ data format: [batch, height, width, channel].
Parameters: - input_var (tf.Tensor) – Input tf.Tensor to the CNN.
- filters (Tuple[Tuple[int, Tuple[int, int]], ..]) – Number and dimension of filters. For example, ((3, (3, 5)), (32, (3, 3))) means there are two convolutional layers. The filter for the first layer have 3 channels and its shape is (3 x 5), while the filter for the second layer have 32 channels and its shape is (3 x 3).
- strides (tuple[int]) – The stride of the sliding window. For example, (1, 2) means there are two convolutional layers. The stride of the filter for first layer is 1 and that of the second layer is 2.
- name (str) – Network name, also the variable scope.
- padding (str) – The type of padding algorithm to use, either ‘SAME’ or ‘VALID’.
- hidden_nonlinearity (callable) – Activation function for intermediate dense layer(s). It should return a tf.Tensor. Set it to None to maintain a linear activation.
- hidden_w_init (callable) – Initializer function for the weight of intermediate dense layer(s). The function should return a tf.Tensor.
- hidden_b_init (callable) – Initializer function for the bias of intermediate dense layer(s). The function should return a tf.Tensor.
Returns: The output tf.Tensor of the CNN.
Return type: tf.Tensor
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cnn_with_max_pooling
(input_var, filters, strides, name, pool_shapes, pool_strides, padding, hidden_nonlinearity=<function relu>, hidden_w_init=<tensorflow.python.ops.init_ops_v2.GlorotUniform object>, hidden_b_init=<tensorflow.python.ops.init_ops_v2.Zeros object>)[source]¶ Convolutional neural network (CNN) with max-pooling.
Note
Based on ‘NHWC’ data format: [batch, height, width, channel].
Parameters: - input_var (tf.Tensor) – Input tf.Tensor to the CNN.
- filters (Tuple[Tuple[int, Tuple[int, int]], ..]) – Number and dimension of filters. For example, ((3, (3, 5)), (32, (3, 3))) means there are two convolutional layers. The filter for the first layer have 3 channels and its shape is (3 x 5), while the filter for the second layer have 32 channels and its shape is (3 x 3).
- strides (tuple[int]) – The stride of the sliding window. For example, (1, 2) means there are two convolutional layers. The stride of the filter for first layer is 1 and that of the second layer is 2.
- name (str) – Model name, also the variable scope of the cnn.
- pool_shapes (tuple[int]) – Dimension of the pooling layer(s). For example, (2, 2) means that all the pooling layers have shape (2, 2).
- pool_strides (tuple[int]) – The strides of the pooling layer(s). For example, (2, 2) means that all the pooling layers have strides (2, 2).
- padding (str) – The type of padding algorithm to use, either ‘SAME’ or ‘VALID’.
- hidden_nonlinearity (callable) – Activation function for intermediate dense layer(s). It should return a tf.Tensor. Set it to None to maintain a linear activation.
- hidden_w_init (callable) – Initializer function for the weight of intermediate dense layer(s). The function should return a tf.Tensor.
- hidden_b_init (callable) – Initializer function for the bias of intermediate dense layer(s). The function should return a tf.Tensor.
Returns: The output tf.Tensor of the CNN.
Return type: tf.Tensor