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def model(X, Y, learning_rate = 0.3, num_iterations = 30000, print_cost = True, lambd = 0, keep_prob = 1): | |
grads = {} | |
costs = [] # to keep track of the cost | |
m = X.shape[1] # number of examples | |
layers_dims = [X.shape[0], 20, 3, 1] | |
# Initialize parameters dictionary. | |
parameters = initialize_parameters(layers_dims) | |
# Loop (gradient descent) | |
for i in range(0, num_iterations): | |
# Forward propagation: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID. | |
if keep_prob == 1: | |
a3, cache = forward_propagation(X, parameters) | |
elif keep_prob < 1: | |
a3, cache = forward_propagation_with_dropout(X, parameters, keep_prob) | |
# Cost function | |
if lambd == 0: | |
cost = compute_cost(a3, Y) | |
else: | |
cost = compute_cost_with_regularization(a3, Y, parameters, lambd) | |
# Backward propagation. | |
assert(lambd==0 or keep_prob==1) # it is possible to use both L2 regularization and dropout, | |
# but this assignment will only explore one at a time | |
if lambd == 0 and keep_prob == 1: | |
grads = backward_propagation(X, Y, cache) | |
elif lambd != 0: | |
grads = backward_propagation_with_regularization(X, Y, cache, lambd) | |
elif keep_prob < 1: | |
grads = backward_propagation_with_dropout(X, Y, cache, keep_prob) | |
# Update parameters. | |
parameters = update_parameters(parameters, grads, learning_rate) | |
# Print the loss every 10000 iterations | |
if print_cost and i % 10000 == 0: | |
print("Cost after iteration {}: {}".format(i, cost)) | |
if print_cost and i % 1000 == 0: | |
costs.append(cost) | |
# plot the cost | |
plt.plot(costs) | |
plt.ylabel('cost') | |
plt.xlabel('iterations (x1,000)') | |
plt.title("Learning rate =" + str(learning_rate)) | |
plt.show() | |
return parameters |
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