I just completed ANN course and started learning CNN. I have basic understanding of padding and stride operation works in CNN.
But have difficultly in mapping input image with neurons in first conv layer but i have basic understanding of how input features are mapped to first hidden layer in ANN.
What is best way of understanding mapping between input image with neurons in first conv layer?
How can I clarify my doubts about the below code example? Code is taken from DL course in Coursera.
def initialize_parameters():
"""
Initializes weight parameters to build a neural network with tensorflow. The shapes are:
W1 : [4, 4, 3, 8]
W2 : [2, 2, 8, 16]
Returns:
parameters -- a dictionary of tensors containing W1, W2
"""
tf.set_random_seed(1) # so that your "random" numbers match ours
### START CODE HERE ### (approx. 2 lines of code)
W1 = tf.get_variable("W1",[4,4,3,8],initializer = tf.contrib.layers.xavier_initializer(seed = 0))
W2 = tf.get_variable("W2",[2,2,8,16],initializer = tf.contrib.layers.xavier_initializer(seed = 0))
### END CODE HERE ###
parameters = {"W1": W1,
"W2": W2}
return parameters
def forward_propagation(X, parameters):
"""
Implements the forward propagation for the model:
CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED
Arguments:
X -- input dataset placeholder, of shape (input size, number of examples)
parameters -- python dictionary containing your parameters "W1", "W2"
the shapes are given in initialize_parameters
Returns:
Z3 -- the output of the last LINEAR unit
"""
# Retrieve the parameters from the dictionary "parameters"
W1 = parameters['W1']
W2 = parameters['W2']
### START CODE HERE ###
# CONV2D: stride of 1, padding 'SAME'
Z1 = tf.nn.conv2d(X,W1, strides = [1,1,1,1], padding = 'SAME')
# RELU
A1 = tf.nn.relu(Z1)
# MAXPOOL: window 8x8, sride 8, padding 'SAME'
P1 = tf.nn.max_pool(A1, ksize = [1,8,8,1], strides = [1,8,8,1], padding = 'SAME')
# CONV2D: filters W2, stride 1, padding 'SAME'
Z2 = tf.nn.conv2d(P1,W2, strides = [1,1,1,1], padding = 'SAME')
# RELU
A2 = tf.nn.relu(Z2)
# MAXPOOL: window 4x4, stride 4, padding 'SAME'
P2 = tf.nn.max_pool(A2, ksize = [1,4,4,1], strides = [1,4,4,1], padding = 'SAME')
# FLATTEN
P2 = tf.contrib.layers.flatten(P2)
# FULLY-CONNECTED without non-linear activation function (not not call softmax).
# 6 neurons in output layer. Hint: one of the arguments should be "activation_fn=None"
Z3 = tf.contrib.layers.fully_connected(P2, 6,activation_fn=None)
### END CODE HERE ###
return Z3
with tf.Session() as sess:
np.random.seed(1)
X, Y = create_placeholders(64, 64, 3, 6)
parameters = initialize_parameters()
Z3 = forward_propagation(X, parameters)
init = tf.global_variables_initializer()
sess.run(init)
a = sess.run(Z3, {X: np.random.randn(1,64,64,3), Y: np.random.randn(1,6)})
print("Z3 = " + str(a))
How is this input image of size 64*64*3 is processed by 8 filter of each size 4*4*3?
stride = 1, padding = same and batch_size = 1.
What I have understood till now is each neuron in first conv layer will have 8 filters and each of them having size 4*4*3. Each neuron in first convolution layer will take portion of the input image which is same as filter size (which is here 4*4*3) and apply the convolution operation and produces eight 64*64 features mapping.
If my understanding is correct then:
1> Why we need striding operation since kernel size and portion input image proceed by each neuron is same, If we apply stride = 1(or 2) then boundary of portion of input image is cross which is something we don't need right ?
2> How do we know which portion of input image (same as kernel size) is mapped which neuron in first conv layer?
If not then:
3> How input image is passed on neurons in first convolution layer, Is is complete input image is passed on to each neuron (Like in fully connected ANN, where all the input features are mapped to each neuron in first hidden layer)?
Or portion of input image ? How do we know which portion of input image is mapped which neuron in first conv layer?
4> Number of kernel specified above example (W1= [4, 4, 3, 8]) is per neuron or total number of kernel in fist conv layer ?
5> how do we know how may neurons used by above example in first convolution layer.
6> Is there any relationship between number of neurons and number of kernel first conv layer.
