Please explain the principles of CNN as an expert in Machine Learning.
A Convolutional Neural Network (CNN) is a type of deep learning algorithm that is used to analyze visual imagery. It is modeled after the structure of the human visual cortex and is composed of multiple layers of neurons that process and extract features from an image. The main principle behind a CNN is that it uses convolutional layers to detect patterns in an image. Each convolutional layer is comprised of a set of filters that detect specific features in an image. These filters are then used to extract features from the image and create a feature map. The feature map is then passed through a pooling layer which reduces the size of the feature map and helps to identify the most important features in the image. Finally, the feature map is passed through a fully-connected layer which classifies the image and outputs the result.
Please introduce the principle of CNN as a machine learning expert.
Convolutional Neural Networks (CNNs) are a type of artificial neural network that are commonly used in image recognition and classification tasks. They are inspired by the structure of the human brain and are composed of multiple layers of neurons connected in a specific pattern. The neurons in the first layer of a CNN are connected to the input image, and the neurons in the last layer are connected to the output. The neurons in between the input and output layers are called feature maps and are responsible for extracting features from the input image. CNNs use convolutional layers to detect patterns in the input image and pooling layers to reduce the size of the feature maps. This allows the CNN to learn the most important features in the image and use them to make predictions.
defwrite_unit_test(function_to_test, unit_test_package = "pytest"): # 解释源代码的步骤 explain_code = """"# How to write great unit tests with {unit_test_package}
In this advanced tutorial for experts, we'll use Python 3.10 and `{unit_test_package}` to write a suite of unit tests to verify the behavior of the following function. ```python {function_to_test} ```
Before writing any unit tests, let's review what each element of the function is doing exactly and what the author's intentions may have been. - First,"""
A good unit test suite should aim to: - Test the function's behavior for a wide range of possible inputs - Test edge cases that the author may not have foreseen - Take advantage of the features of `{unit_test_package}` to make the tests easy to write and maintain - Be easy to read and understand, with clean code and descriptive names - Be deterministic, so that the tests always pass or fail in the same way
`{unit_test_package}` has many convenient features that make it easy to write and maintain unit tests. We'll use them to write unit tests for the function above.
For this particular function, we'll want our unit tests to handle the following diverse scenarios (and under each scenario, we include a few examples as sub-bullets): -""" test_plan_template = PromptTemplate( input_variables=["unit_test_package", "function_to_test", "code_explaination"], template= explain_code + "{code_explaination}" + test_plan ) test_plan_llm = OpenAI(model_name="text-davinci-002", temperature=0.4, max_tokens=1000, top_p=1, stop=["\n\n", "\n\t\n", "\n \n"]) test_plan_step = LLMChain(llm=test_plan_llm, prompt=test_plan_template, output_key="test_plan")
# 撰写测试代码的步骤 starter_comment = "Below, each test case is represented by a tuple passed to the @pytest.mark.parametrize decorator" prompt_to_generate_the_unit_test = """
Before going into the individual tests, let's first look at the complete suite of unit tests as a cohesive whole. We've added helpful comments to explain what each line does. ```python import {unit_test_package} # used for our unit tests
defformat_time(seconds): minutes, seconds = divmod(seconds, 60) hours, minutes = divmod(minutes, 60) if hours > 0: returnf"{hours}h{minutes}min{seconds}s" elif minutes > 0: returnf"{minutes}min{seconds}s" else: returnf"{seconds}s" #Below, each test case is represented by a tuple passed to the @pytest.mark.parametrize decorator. #The first element of the tuple is the name of the test case, and the second element is a list of tuples, #where each tuple contains the input values for the format_time() function and the expected output. @pytest.mark.parametrize("test_case, input_values, expected_output", [ # Test cases for when the seconds parameter is an integer ("seconds is positive", (42,), "42s"), ("seconds is negative", (-42,), "-42s"), ("seconds is 0", (0,), "0s"), # Test cases for when the seconds parameter is not an integer ("seconds is a float", (42.0,), "42.0s"), ("seconds is a string", ("42",), "42s"), ("seconds is None", (None,), "None"), # Test cases for when the seconds parameter is an integer, but it is not in the range 0-3600 ("seconds is too small", (-1,), "-1s"), ("seconds is too large", (3601,), "1h0min1s"), ]) deftest_format_time(test_case, input_values, expected_output): # We use the pytest.raises context manager to assert that the function raises a TypeError # if the input is not an integer. with pytest.raises(TypeError): format_time(input_values) # We use the pytest.approx context manager to assert that the output is approximately equal # to the expected output, within a certain tolerance. assert format_time(input_values) == pytest.approx(expected_output)
