# please, uncomment the week you're working on
# setup_google_colab.setup_week1()
# setup_google_colab.setup_week2()
setup_google_colab.setup_week3()
# setup_google_colab.setup_week4()
# setup_google_colab.setup_week5()
# setup_google_colab.setup_week6()
import sys
sys.path.append("..")
import grading
import download_utils
# !!! remember to clear session/graph if you rebuild your graph to avoid out-of-memory errors !!!
download_utils.link_all_keras_resources()
import tensorflow as tf
import keras
from keras import backend as K
import numpy as np
# %matplotlib inline
import matplotlib.pyplot as plt
print(tf.__version__)
print(keras.__version__)
import grading_utils
import keras_utils
from keras_utils import reset_tf_session
"""# Fill in your Coursera token and email
To successfully submit your answers to our grader, please fill in your Coursera submission token and email
"""
Your very own neural network
In this notebook we're going to build a neural network using naught but pure numpy and steel nerves. It's going to be fun, I promise!
In [1]:
import sys
sys.path.append("..")
import tqdm_utils
import download_utils
In [2]:
# use the preloaded keras datasets and models
download_utils.link_all_keras_resources()
In [3]:
from __future__ import print_function
import numpy as np
np.random.seed(42)
Here goes our main class: a layer that can do .forward() and .backward() passes.
In [4]:
class Layer:
"""
A building block. Each layer is capable of performing two things:
- Process input to get output: output = layer.forward(input)
- Propagate gradients through itself: grad_input = layer.backward(input, grad_output)
Some layers also have learnable parameters which they update during layer.backward.
"""
def __init__(self):
