markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
|---|---|---|---|---|---|
*Note*: the brackets are optional | t = 1, 2, 3, 4
t | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
"Unpacking": as with lists (or any iterable), it is possible to extract values in a tuple and assign them to new variables | t[1:3]
second_item, third_item = t[1], t[2]
print(second_item)
print(third_item) | 2
3
| CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
**Tip**: unpack undefined number of items | second_item, *greater_items = t[1:]
second_item
greater_items | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
DictionnariesMap keys to values | d = {'key1': 0, 'key2': 1}
d | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Keys must be unique.But be careful: no error is raised if you provide multiple, identical keys! | d = {'key1': 0, 'key2': 1, 'key1': 3}
d | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Indexing dictionnaries by key | d['key1'] | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Keys are not limited to strings, they can be many things (but not anything, we'll see later) | d = {'key1': 0, 2: 1, 3.: 3}
d[2] | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Get keys or values | d.keys()
d.values()
a[d['key1']]
d = {
'benoit': {
'age': 33,
'section':'5.5'
}
}
d['benoit']['age'] | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Mutable vs. immutable We can change the value of a variable in place (after we create the variable) or we can't. For example, lists are mutable. | a = [1, 2, 3, 4]
a | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Change the value of one item in place | a[0] = 'one'
a | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Append one item at the end of the list | a.append(5)
a | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Insert one item at a given position | a.insert(0, 'zero')
a | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Extract and remove the last item | a.pop()
a | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Dictionnaries are mutable (note the order of the keys in the printed dict) | d = {'key1': 0, 'key2': 1, 'key3': 2}
d['key4'] = 4
d | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Pop an item of given key | d.pop('key1')
d | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Tuples are immutable! | t = (1, 2, 3, 4)
t.append(5) | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Strings are immutable! | food = "bradwurst"
food[0:4] = "cury" | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
But is easy and efficient to create new strings | food = "curry" + food[-5:]
food | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
A reason why strings are immutable?The keys of a dictionnary cannot be mutable, e.g., we cannot not use a list | d = {[1, 3]: 0} | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
The keys of a dictionnary cannot be mutable, for a quite obvious reason that it is used as indexes, like in a database. If we allow changing the indexes, it can be a real mess!If strings were mutable, then we could'nt use it as keys in dictionnaries.*Note*: more precisely, keys of a dictionnary must be "hashable". Var... | a = [1, 2, 3]
b = a
b[0] = 'one'
a | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Explanation: the concept of variable is different in Python than in, e.g., C or Fortran`a = [1, 2, 3]` means we create a list object and we bind this object to a name (label or identifier) "a"`b = a` means we bind the same object to a new name "b"You can find more details and good illustrations here: https://nedbatchel... | id(a)
id(b) | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
`is` : check whether two identifiers are bound to the same value (object) | a is b | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
OK, but how do you explain this? | a = 1
b = a
b = 2
a
a is b
id(a)
id(b) | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Can you explain what's going on here? | a = 1
b = 2
b = a + b
b | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Where does go the value "2" that was initially bounded to "b"? OK, now what about this? Very confusing! | a = 1
b = 1
a is b
a = 1.
b = 1.
a is b | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Dynamic, strong, duck typing Dynamic typing: no need to explicitly declare a type of an object/variable before using it. This is done automatically depending on the given object/value. | a = 1
type(a) | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Strong typing: Converting from one type to another must be explicit, i.e., a value of a given type cannot be magically converted into another type | a + '1'
a + int('1')
eval('1 + 2 * 3') | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
An exception: integer to float casting | a + 1. | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Duck typing: The type of an object doesn't really matter. What an object can or cannot do is more important.> "If it walks like a duck and it quacks like a duck, then it must be a duck" For example, we can show that iterating trough list, string or dict can be done using the exact same loop | var = [1, 2, 3, 4]
for i in var:
print(i)
var = 'abcd'
for i in var:
print(i)
var = {'key1': 1, 'key2': 2}
for i in var:
print(i) | key1
key2
| CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
In the last case, iterating a dictionnary uses the keys.It is possible to iterate the values: | for v in var.values():
print(v) | 1
2
| CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Or more useful, iterate trough both keys and values | for k, v in var.items():
print(k, v)
t = ('key1', 1)
k, v = t
var.items() | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Arithmetic operators can be obviously applied on integer, float... | 1 + 1
1 + 2. | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
...but also on strings and lists (in this case it does concatenation) | [1, 2, 3] + ['a', 'b', 'c']
'other' + 'one' | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
... and also mixing the types, e.g., repeat sequence x times | [1, 2, 3] * 3
'one' * 3 | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
...although, everything is not possible | [1, 2, 3] * 3.5 | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Boolean: what is True and what is False | print(True)
print(False)
print(bool(0))
print(bool(-1))
a = 1.7
if a:
print('non zero')
print(bool(''))
print(bool('no empty'))
print(bool([]))
print(bool([1, 2]))
print(bool({}))
print(bool({'key1': 1}))
d = {}
if not d:
print('there is no item')
| there is no item
| CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
list comprehension Example: we create a list from another one using a `for` loop | ints = [1, 3, 5, 0, 2, 0]
true_or_false = []
for i in ints:
true_or_false.append(bool(i))
true_or_false | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
But there is a much more succint way to do it. It is still (and maybe even more) readable | true_or_false = [bool(i) for i in ints]
true_or_false | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
More complex example, with conditions | float_no3 = [float(i) for i in ints if i != 3]
float_no3 | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Other kinds of conditions(It starts to be less readable -> don't abuse list comprehension) | float_str3 = [float(i) if i != 3 else str(i) for i in ints]
float_str3 | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Dict comprehensions | int2float_map = {i: float(i) for i in ints}
int2float_map | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
FunctionsA function take value(s) as input and (optionally) return value(s) as outputinputs = arguments | def add(a, b):
"""Add two things."""
return a + b
def print_the_argument(arg):
print(arg)
print_the_argument('a string') | a string
| CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
We can call it several times with different values | add(1, 3)
help(add) | Help on function add in module __main__:
add(a, b)
Add two things.
| CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Nested calls | add(add(1, 2), 3) | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Duck typing is really useful! A single function for doing many things (write less code) | add(1., 2.)
add('one', 'two')
add([1, 2, 3], [1, 2, 3]) | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Functions have a scope that is local | a = 1
def func():
a = 2
a
func()
a | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Call by value? | def func(j):
j = j + 1
print('inside: ', j)
return j
i = 1
print('before:', i)
i = func(i)
print('after:', i) | before: 1
inside: 2
after: 2
| CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Not really... | def func(li):
li[0] = 1000
print('inside: ', li[0])
li = [1]
print('before:', li[0])
func(li)
print('after:', li[0]) | before: 1
inside: 1000
after: 1000
| CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Composing functions (start to look like functional programming) | C2K_OFFSET = 273.15
def fahr_to_kelvin(temp):
"""convert temp from fahrenheit to kelvin"""
return ((temp - 32) * (5/9)) + C2K_OFFSET
def kelvin_to_celsius(temp_k):
# convert temperature from kevin to celsius
return temp_k - C2K_OFFSET
def fahr_to_celsius(temp_f):
temp_k = fahr_to_kelvin(temp_f)
... | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Function docstring (help) Default argument values (keyword arguments) | def display(a=1, b=2, c=3):
print(a, b, c)
display(b=4) | 1 4 3
| CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
When calling a function, the order of the keyword arguments doesn't matterBut the order matters for positional arguments!! | display(c=5, a=1)
display(3) | 3 2 3
| CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Mix positional and keyword arguments: positional arguments must be added before keyword arguments | def display(c, a=1, b=2):
print(a, b, c)
display(1000) | 1 2 1000
| CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
What's going on here? | def add_to_list(li=[], value=1):
li.append(value)
return li
add_to_list()
add_to_list()
add_to_list() | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Try running again the cell that defines the function, and then the cells that call the functionThis is sooo confusing! So you shouldn't use mutable objects as default valuesWorkaround: | def add_to_list(li=None, value=1):
if li is None:
li = []
li.append(value)
return li
add_to_list()
add_to_list() | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Arbitrary number of arguments | def display_args(*args):
print(args)
nb_args = len(args)
print(nb_args)
print(*args)
display_args('one')
display_args(1, '2', 'bradwurst') | (1, '2', 'bradwurst')
3
1 2 bradwurst
| CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Arbitrary number of keyword arguments | def display_args_kwargs(*args, **kwargs):
print(*args)
print(kwargs)
display_args_kwargs('one', 2, three=3.) | one 2
{'three': 3.0}
| CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Return more than one value (tuple) | def spherical_coords(x, y, z):
# convert
return r, theta, phi | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
ModulesModules are Python code in (`.py`) files that can be imported from within Python.Like functions, it allows to reusing the code in different contexts. Write a module with the temperature conversion functions above(note: the `%%writefile` is a magic cell command in the notebook that writes the content of the cel... | %%writefile temp_converter.py
C2K_OFFSET = 273.15
def fahr_to_kelvin(temp):
"""convert temp from fahrenheit to kelvin"""
return ((temp - 32) * (5/9)) + C2K_OFFSET
def kelvin_to_celsius(temp_k):
# convert temperature from kevin to celsius
return temp_k - C2K_OFFSET
def fahr_to_celsius(temp_f):
te... | Overwriting temp_converter.py
| CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Import a module | import temp_converter | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Access the functions imported with the module using the module name as a "namespace"**Tip**: imported module + dot + for autocompletion | temp_converter.fahr_to_celsius(100.) | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Import the module with a (short) alias for the namespace | import temp_converter as tc
tc.fahr_to_celsius(100.) | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Import just a function from the module | from temp_converter import fahr_to_celsius
fahr_to_celsius(100.) | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Import everything in the module (without using a namespace)Strongly discouraged!! Name conflicts! | from temp_converter import *
kelvin_to_celsius(270) | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
(Text) file IOLet's create a small file with some data | %%writefile data.csv
"depth", "some_variable"
200, 2.4e2
400, 5.6e2
600, 2.6e8 | Writing data.csv
| CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Open the file using Python: | f = open("data.csv", "r")
f | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Read the content | raw_data = f.readlines()
raw_data | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
What happens here? | f.readlines()
f.seek(0)
f.readlines() | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
Close the file | f.close() | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
It is safer to use the `with` statement (contexts) | with open("data.csv") as f:
raw_data = f.readlines()
raw_data
f.closed | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
We don't need to close the file, it is done automatically after executing the block of instructions under the `with` statement It is safer because if an error happens within the block of instructions, the file is closed anyway.Note here how we can explicitly raise an Error. There are many kinds of exceptions, see: http... | with open("data.csv") as f:
raw_data = f.readlines()
raise ValueError("something wrong happened")
raw_data
f.closed | _____no_output_____ | CC-BY-4.0 | notebooks/lectures_potsdam_201802/python_intro.ipynb | benbovy/python_short_course |
14 - Introduction to Deep Learningby [Alejandro Correa Bahnsen](albahnsen.com/)version 0.1, May 2016 Part of the class [Machine Learning Applied to Risk Management](https://github.com/albahnsen/ML_RiskManagement)This notebook is licensed under a [Creative Commons Attribution-ShareAlike 3.0 Unported License](http://cr... | import numpy as np
from load import mnist
X_train, X_test, y_train2, y_test2 = mnist(onehot=True)
y_train = np.argmax(y_train2, axis=1)
y_test = np.argmax(y_test2, axis=1)
X_train[1].reshape((28, 28)).round(0).astype(int)[:, 4:26].tolist()
from pylab import imshow, show, cm
import matplotlib.pylab as plt
%matplotlib in... | _____no_output_____ | MIT | notebooks/14_Intro_DeepLearning.ipynb | Torroledo/ML_RiskManagement |
Naive modelFor each image, find the “most similar” image and guessthat as the label | def similarity(image, images):
similarities = []
image = image.reshape((28, 28))
images = images.reshape((-1, 28, 28))
for i in range(images.shape[0]):
distance = np.sqrt(np.sum(image - images[i]) ** 2)
sim = 1 / distance
similarities.append(sim)
return similarities
np.random... | _____no_output_____ | MIT | notebooks/14_Intro_DeepLearning.ipynb | Torroledo/ML_RiskManagement |
Lets try an other example | view_image(X_test[200])
similarities = similarity(X_test[200], X_train[small_train])
view_image(X_train[small_train[np.argmax(similarities)]]) | _____no_output_____ | MIT | notebooks/14_Intro_DeepLearning.ipynb | Torroledo/ML_RiskManagement |
Logistic RegressionLogistic regression is a probabilistic, linear classifier. It is parametrizedby a weight matrix $W$ and a bias vector $b$ Classification isdone by projecting data points onto a set of hyperplanes, the distance towhich is used to determine a class membership probability.Mathematically, this can be wr... | import theano
from theano import tensor as T
import numpy as np
import datetime as dt
theano.config.floatX = 'float32' | _____no_output_____ | MIT | notebooks/14_Intro_DeepLearning.ipynb | Torroledo/ML_RiskManagement |
```Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray). Using Theano it is possible to attain speeds rivaling hand-crafted C implementations for problems involving large amounts of data. It can also surpass C ... | def floatX(X):
# return np.asarray(X, dtype='float32')
return np.asarray(X, dtype=theano.config.floatX)
def init_weights(shape):
return theano.shared(floatX(np.random.randn(*shape) * 0.01))
def model(X, w):
return T.nnet.softmax(T.dot(X, w))
X = T.fmatrix()
Y = T.fmatrix()
w = init_weights((784, 10))... | _____no_output_____ | MIT | notebooks/14_Intro_DeepLearning.ipynb | Torroledo/ML_RiskManagement |
initialize model | py_x = model(X, w)
y_pred = T.argmax(py_x, axis=1)
cost = T.mean(T.nnet.categorical_crossentropy(py_x, Y))
gradient = T.grad(cost=cost, wrt=w)
update = [[w, w - gradient * 0.05]]
train = theano.function(inputs=[X, Y], outputs=cost, updates=update, allow_input_downcast=True)
predict = theano.function(inputs=[X], outputs... | _____no_output_____ | MIT | notebooks/14_Intro_DeepLearning.ipynb | Torroledo/ML_RiskManagement |
One iteration | for start, end in zip(range(0, X_train.shape[0], 128), range(128, X_train.shape[0], 128)):
cost = train(X_train[start:end], y_train2[start:end])
errors = [(np.mean(y_train != predict(X_train)),
np.mean(y_test != predict(X_test)))]
errors | _____no_output_____ | MIT | notebooks/14_Intro_DeepLearning.ipynb | Torroledo/ML_RiskManagement |
Now for 100 epochs | t0 = dt.datetime.now()
for i in range(100):
for start, end in zip(range(0, X_train.shape[0], 128),
range(128, X_train.shape[0], 128)):
cost = train(X_train[start:end], y_train2[start:end])
errors.append((np.mean(y_train != predict(X_train)),
... | _____no_output_____ | MIT | notebooks/14_Intro_DeepLearning.ipynb | Torroledo/ML_RiskManagement |
Checking the results | y_pred = predict(X_test)
np.random.seed(2)
small_test = np.random.choice(X_test.shape[0], 10)
for i in small_test:
view_image(X_test[i], label=y_test[i], predicted=y_pred[i], size=1) | _____no_output_____ | MIT | notebooks/14_Intro_DeepLearning.ipynb | Torroledo/ML_RiskManagement |
Simple Neural NetAdd a hidden layer with a sigmoid activation function | def sgd(cost, params, lr=0.05):
grads = T.grad(cost=cost, wrt=params)
updates = []
for p, g in zip(params, grads):
updates.append([p, p - g * lr])
return updates
def model(X, w_h, w_o):
h = T.nnet.sigmoid(T.dot(X, w_h))
pyx = T.nnet.softmax(T.dot(h, w_o))
return pyx
w_h = init_weig... | _____no_output_____ | MIT | notebooks/14_Intro_DeepLearning.ipynb | Torroledo/ML_RiskManagement |
Complex Neural NetTwo hidden layers with dropout | from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
srng = RandomStreams()
def rectify(X):
return T.maximum(X, 0.) | _____no_output_____ | MIT | notebooks/14_Intro_DeepLearning.ipynb | Torroledo/ML_RiskManagement |
Understanding rectifier units | def RMSprop(cost, params, lr=0.001, rho=0.9, epsilon=1e-6):
grads = T.grad(cost=cost, wrt=params)
updates = []
for p, g in zip(params, grads):
acc = theano.shared(p.get_value() * 0.)
acc_new = rho * acc + (1 - rho) * g ** 2
gradient_scaling = T.sqrt(acc_new + epsilon)
g = g /... | _____no_output_____ | MIT | notebooks/14_Intro_DeepLearning.ipynb | Torroledo/ML_RiskManagement |
RMSpropRMSprop is an unpublished, adaptive learning rate method proposed by Geoff Hinton in [Lecture 6e of his Coursera Class](http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)RMSprop and Adadelta have both been developed independently around the same time stemming from the need to resolve Adagr... | def dropout(X, p=0.):
if p > 0:
retain_prob = 1 - p
X *= srng.binomial(X.shape, p=retain_prob, dtype=theano.config.floatX)
X /= retain_prob
return X
def model(X, w_h, w_h2, w_o, p_drop_input, p_drop_hidden):
X = dropout(X, p_drop_input)
h = rectify(T.dot(X, w_h))
h = dropou... | _____no_output_____ | MIT | notebooks/14_Intro_DeepLearning.ipynb | Torroledo/ML_RiskManagement |
Convolutional Neural NetworkIn machine learning, a convolutional neural network (CNN, or ConvNet) is a type of feed-forward artificial neural network in which the connectivity pattern between its neurons is inspired by the organization of the animal visual cortex, whose individual neurons are arranged in such a way th... | # from theano.tensor.nnet.conv import conv2d
from theano.tensor.nnet import conv2d
from theano.tensor.signal.downsample import max_pool_2d | /home/al/anaconda3/lib/python3.5/site-packages/theano/tensor/signal/downsample.py:6: UserWarning: downsample module has been moved to the theano.tensor.signal.pool module.
"downsample module has been moved to the theano.tensor.signal.pool module.")
| MIT | notebooks/14_Intro_DeepLearning.ipynb | Torroledo/ML_RiskManagement |
Modify dropout function | def model(X, w, w2, w3, w4, w_o, p_drop_conv, p_drop_hidden):
l1a = rectify(conv2d(X, w, border_mode='full'))
l1 = max_pool_2d(l1a, (2, 2))
l1 = dropout(l1, p_drop_conv)
l2a = rectify(conv2d(l1, w2))
l2 = max_pool_2d(l2a, (2, 2))
l2 = dropout(l2, p_drop_conv)
l3a = rectify(conv2d(l2, w3))
... | _____no_output_____ | MIT | notebooks/14_Intro_DeepLearning.ipynb | Torroledo/ML_RiskManagement |
reshape into conv 4tensor (b, c, 0, 1) format | X_train2 = X_train.reshape(-1, 1, 28, 28)
X_test2 = X_test.reshape(-1, 1, 28, 28)
# now 4tensor for conv instead of matrix
X = T.ftensor4()
Y = T.fmatrix()
w = init_weights((32, 1, 3, 3))
w2 = init_weights((64, 32, 3, 3))
w3 = init_weights((128, 64, 3, 3))
w4 = init_weights((128 * 3 * 3, 625))
w_o = init_weights((625, ... | _____no_output_____ | MIT | notebooks/14_Intro_DeepLearning.ipynb | Torroledo/ML_RiskManagement |
Predict batches of images | tf.compat.v1.enable_v2_behavior()
label = ['3_24+10', '3_24+30', '3_24+5', '3_24+60', '3_24+70', '3_24+90', '3_24+110', '3_24+20', '3_24+40', '3_24+50', '3_24+80', '1_12_1', '1_12_2', '1_13', '1_14', '1_19', '1_24', '1_26', '1_27', '3_21', '3_31', '3_33', '4_4_1', '4_4_2', '4_5_2', '4_5_4', '4_5_5', '4_8_5', '4_8_6', '... | _____no_output_____ | MIT | predict.ipynb | trancongthinh6304/trafficsignclassification |
Predict single image | model1= keras.models.load_model("../input/aaaaaaaaaa/VGG19_2.h5")
model2= keras.models.load_model("../input/aaaaaaaaaa/InceptionResNetV2_2.h5")
model3 = keras.models.load_model('../input/aaaaaaaaaa/denset201_2.h5')
def auto_encoder(img_path):
img = image.load_img(img_path, target_size=(80,80,3))
img = image.img... | _____no_output_____ | MIT | predict.ipynb | trancongthinh6304/trafficsignclassification |
ML Project 6033657523 - Feedforward neural network Importing the libraries | from sklearn.metrics import mean_absolute_error
from sklearn.svm import SVR
from sklearn.model_selection import KFold, train_test_split
from math import sqrt
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error, mean_absolute_error
import matplotlib.pyplot as plt | _____no_output_____ | MIT | ML Project Feedforward Neural Network 6033657523.ipynb | bellmcp/machine-learning-price-prediction |
Importing the cleaned dataset | dataset = pd.read_csv('cleanData_Final.csv')
X = dataset[['PrevAVGCost', 'PrevAssignedCost', 'AVGCost', 'LatestDateCost', 'A', 'B', 'C', 'D', 'E', 'F', 'G']]
y = dataset['GenPrice']
X | _____no_output_____ | MIT | ML Project Feedforward Neural Network 6033657523.ipynb | bellmcp/machine-learning-price-prediction |
Splitting the dataset into the Training set and Test set | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0) | _____no_output_____ | MIT | ML Project Feedforward Neural Network 6033657523.ipynb | bellmcp/machine-learning-price-prediction |
Feedforward neural network Fitting Feedforward neural network to the Training Set | from sklearn.neural_network import MLPRegressor
regressor = MLPRegressor(hidden_layer_sizes = (200, 200, 200, 200, 200), activation = 'relu', solver = 'adam', max_iter = 500, learning_rate = 'adaptive')
regressor.fit(X_train, y_train)
trainSet = pd.concat([X_train, y_train], axis = 1)
trainSet.head() | _____no_output_____ | MIT | ML Project Feedforward Neural Network 6033657523.ipynb | bellmcp/machine-learning-price-prediction |
Evaluate model accuracy | y_pred = regressor.predict(X_test)
y_pred
testSet = pd.concat([X_test, y_test], axis = 1)
testSet.head() | _____no_output_____ | MIT | ML Project Feedforward Neural Network 6033657523.ipynb | bellmcp/machine-learning-price-prediction |
Compare GenPrice with PredictedGenPrice | datasetPredict = pd.concat([testSet.reset_index(), pd.Series(y_pred, name = 'PredictedGenPrice')], axis = 1).round(2)
datasetPredict.head(10)
datasetPredict.corr()
print("Training set accuracy = " + str(regressor.score(X_train, y_train)))
print("Test set accuracy = " + str(regressor.score(X_test, y_test))) | Training set accuracy = 0.9898465392908009
Test set accuracy = 0.9841771850834575
| MIT | ML Project Feedforward Neural Network 6033657523.ipynb | bellmcp/machine-learning-price-prediction |
Training set accuracy = 0.9885445650077587Test set accuracy = 0.9829187423043221 MSE | from sklearn import metrics
print('MSE:', metrics.mean_squared_error(y_test, y_pred)) | MSE: 160.2404730229541
| MIT | ML Project Feedforward Neural Network 6033657523.ipynb | bellmcp/machine-learning-price-prediction |
MSE v1: 177.15763887557458MSE v2: 165.73161615532584MSE v3: 172.98494783761967 MAPE | def mean_absolute_percentage_error(y_test, y_pred):
y_test, y_pred = np.array(y_test), np.array(y_pred)
return np.mean(np.abs((y_test - y_pred)/y_test)) * 100
print('MAPE:', mean_absolute_percentage_error(y_test, y_pred)) | MAPE: 6.159884199380194
| MIT | ML Project Feedforward Neural Network 6033657523.ipynb | bellmcp/machine-learning-price-prediction |
MAPE v1: 6.706572320387714MAPE v2: 6.926678067146115MAPE v3: 7.34081953098462 Visualize | import matplotlib.pyplot as plt
plt.plot([i for i in range(len(y_pred))], y_pred, color = 'r')
plt.scatter([i for i in range(len(y_pred))], y_test, color = 'b')
plt.ylabel('Price')
plt.xlabel('Index')
plt.legend(['Predict', 'True'], loc = 'best')
plt.show() | _____no_output_____ | MIT | ML Project Feedforward Neural Network 6033657523.ipynb | bellmcp/machine-learning-price-prediction |
Transfer LearningMost of the time you won't want to train a whole convolutional network yourself. Modern ConvNets training on huge datasets like ImageNet take weeks on multiple GPUs. Instead, most people use a pretrained network either as a fixed feature extractor, or as an initial network to fine tune. In this notebo... | from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
vgg_dir = 'tensorflow_vgg/'
# Make sure vgg exists
if not isdir(vgg_dir):
raise Exception("VGG directory doesn't exist!")
class DLProgress(tqdm):
last_block = 0
def hook(self, block_num=1, block_size=1, total_s... | Parameter file already exists!
| MIT | transfer-learning/Transfer_Learning.ipynb | skagrawal/Deep-Learning-Udacity-ND |
Flower powerHere we'll be using VGGNet to classify images of flowers. To get the flower dataset, run the cell below. This dataset comes from the [TensorFlow inception tutorial](https://www.tensorflow.org/tutorials/image_retraining). | import tarfile
dataset_folder_path = 'flower_photos'
class DLProgress(tqdm):
last_block = 0
def hook(self, block_num=1, block_size=1, total_size=None):
self.total = total_size
self.update((block_num - self.last_block) * block_size)
self.last_block = block_num
if not isfile('flower_ph... | _____no_output_____ | MIT | transfer-learning/Transfer_Learning.ipynb | skagrawal/Deep-Learning-Udacity-ND |
ConvNet CodesBelow, we'll run through all the images in our dataset and get codes for each of them. That is, we'll run the images through the VGGNet convolutional layers and record the values of the first fully connected layer. We can then write these to a file for later when we build our own classifier.Here we're usi... | import os
import numpy as np
import tensorflow as tf
from tensorflow_vgg import vgg16
from tensorflow_vgg import utils
data_dir = 'flower_photos/'
contents = os.listdir(data_dir)
classes = [each for each in contents if os.path.isdir(data_dir + each)] | _____no_output_____ | MIT | transfer-learning/Transfer_Learning.ipynb | skagrawal/Deep-Learning-Udacity-ND |
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