創建placeholder:
inputs = tf.placeholder(dtype='float', shape=[None, 224, 224, 3], name='inputs')
初始化參數(xavier方法)：
tf.set_random_seed(1)
W1 = tf.get_variable('W1', [4, 4, 3, 8], initializer=tf.contrib.layers.xavier_initializer(seed=0))
神經網絡層：
# convolution layer
tf.nn.conv2d(X, W1, strides=[1, s, s, 1], padding='SAME')
# max pooling layer
tf.nn.max_pool(A, ksize=[1, f, f, 1], strides=[1, s, s, 1], padding='SAME')
# relu layer
tf.nn.relu(Z)
# flatten layer
tf.contrib.layers.flatten(P)
# fully connected layer, F is a flattened input
# 由 tensorflow 自動初始化
tf.contrib.layers.fully_connected(F, num_outputs)
計算損失：
# 計算了 softmax 和 resulting cost
# logits 和 labels 均是 one hot 形式
# 默認 shape 是: [batch_size, num_classes]
cost = tf.nn.softmax_cross_entropy_with_logits(logits, labels)
# 計算 batch_size 個 cost 的均值
cost = tf.reduce_mean(cost)
優化：
# Adam 優化器 最小化 cost
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)
預測：
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(inputs_Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
# eval(
# feed_dict=None, session=None
# )
# Evaluates this tensor in a Session.
# Calling this method will execute all preceding operations that produce the inputs needed # for the operation that produces this tensor.
# N.B. Before invoking Tensor.eval(), its graph must have been launched in a session, and either a default session must be available, or session must be specified explicitly.
train_accuracy = accuracy.eval({inputs_X: X_train, inputs_Y: Y_train})
例子：
# GRADED FUNCTION: initialize_parameters
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
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))
parameters = {"W1": W1,
"W2": W2}
return parameters
# GRADED FUNCTION: forward_propagation
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']
# 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
F = tf.contrib.layers.flatten(P2)
# FULLY-CONNECTED without non-linear activation function (not not call softmax).
Z3 = tf.contrib.layers.fully_connected(F, 6, activation_fn=None)
# 6 neurons in output layer. Hint: one of the arguments should be "activation_fn=None"
return Z3
# GRADED FUNCTION: compute_cost
def compute_cost(Z3, Y):
"""
Computes the cost
Arguments:
Z3 -- output of forward propagation (output of the last LINEAR unit), of shape (6, number of examples)
Y -- "true" labels vector placeholder, same shape as Z3
Returns:
cost - Tensor of the cost function
"""
cost = tf.nn.softmax_cross_entropy_with_logits(logits=Z3, labels=Y)
cost = tf.reduce_mean(cost)
return cost
# GRADED FUNCTION: model
def model(X_train, Y_train, X_test, Y_test, learning_rate = 0.009,
num_epochs = 180, minibatch_size = 64, print_cost = True):
"""
Implements a three-layer ConvNet in Tensorflow:
CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED
Arguments:
X_train -- training set, of shape (None, 64, 64, 3)
Y_train -- test set, of shape (None, n_y = 6)
X_test -- training set, of shape (None, 64, 64, 3)
Y_test -- test set, of shape (None, n_y = 6)
learning_rate -- learning rate of the optimization
num_epochs -- number of epochs of the optimization loop
minibatch_size -- size of a minibatch
print_cost -- True to print the cost every 100 epochs
Returns:
train_accuracy -- real number, accuracy on the train set (X_train)
test_accuracy -- real number, testing accuracy on the test set (X_test)
parameters -- parameters learnt by the model. They can then be used to predict.
"""
ops.reset_default_graph() # to be able to rerun the model without overwriting tf variables
tf.set_random_seed(1) # to keep results consistent (tensorflow seed)
seed = 3 # to keep results consistent (numpy seed)
(m, n_H0, n_W0, n_C0) = X_train.shape
n_y = Y_train.shape[1]
costs = [] # To keep track of the cost
# Create Placeholders of the correct shape
inputs_X = tf.placeholder(dtype='float', shape=(None, n_H0, n_W0, n_C0))
inputs_Y = tf.placeholder(dtype='float', shape=[None, n_y])
# Initialize parameters
parameters = initialize_parameters()
# Forward propagation: Build the forward propagation in the tensorflow graph
Z3 = forward_propagation(inputs_X, parameters)
# Cost function: Add cost function to tensorflow graph
cost = compute_cost(Z3, inputs_Y)
# Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer that minimizes the cost.
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)
# Initialize all the variables globally
init = tf.global_variables_initializer()
# Start the session to compute the tensorflow graph
with tf.Session() as sess:
# Run the initialization
sess.run(init)
# Do the training loop
for epoch in range(num_epochs):
minibatch_cost = 0.
num_minibatches = int(m / minibatch_size) # number of minibatches of size minibatch_size in the train set
seed = seed + 1
minibatches = random_mini_batches(X_train, Y_train, minibatch_size, seed)
for minibatch in minibatches:
# Select a minibatch
(minibatch_X, minibatch_Y) = minibatch
# IMPORTANT: The line that runs the graph on a minibatch.
# Run the session to execute the optimizer and the cost, the feedict should contain a minibatch for (X,Y).
temp_cost, _ = sess.run([cost, optimizer], feed_dict={inputs_X: minibatch_X,
inputs_Y: minibatch_Y})
minibatch_cost += temp_cost / num_minibatches
# Print the cost every epoch
if print_cost == True and epoch % 5 == 0:
print ("Cost after epoch %i: %f" % (epoch, minibatch_cost))
if print_cost == True and epoch % 1 == 0:
costs.append(minibatch_cost)
# plot the cost
plt.plot(np.squeeze(costs))
plt.ylabel('cost')
plt.xlabel('iterations (per tens)')
plt.title("Learning rate =" + str(learning_rate))
plt.show()
# Calculate the correct predictions
correct_prediction = tf.equal(tf.argmax(Z3, 1), tf.argmax(inputs_Y, 1))
# Calculate accuracy on the test set
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print(accuracy)
train_accuracy = accuracy.eval({inputs_X: X_train, inputs_Y: Y_train})
test_accuracy = accuracy.eval({inputs_X: X_test, inputs_Y: Y_test})
print("Train Accuracy:", train_accuracy)
print("Test Accuracy:", test_accuracy)
return train_accuracy, test_accuracy, parameters