Hi,

And even with the help of SOF I still cannot reproduce the numbers you've got in your git. I'm getting the following:

Epoch 1/5
13013/13013 [==============================] - 17s 1ms/step - loss: 825128.0000 - acc: 0.8998 - val_loss: 211068.5156 - val_acc: 0.9019
Epoch 2/5
13013/13013 [==============================] - 16s 1ms/step - loss: 766153.7500 - acc: 0.9068 - val_loss: 582742.2500 - val_acc: 0.8027

ImportError Traceback (most recent call last)
in ()
1 from keras.models import Sequential
2 from keras.layers import Dense, Activation
----> 3 from keras.optimizers import SGD

ImportError: cannot import name 'SGD' from 'keras.optimizers' (/usr/local/lib/python3.7/dist-packages/keras/optimizers.py)

NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.

To view examples of installing some common dependencies, click the
"Open Examples" button below.

Hi,

At page 54 of this book, there is a formula for counting origin accounts have insufficient funds. As the code below:

     len(dfOdd[(dfOdd.oldBalanceOrig <= dfOdd.amount)]) / len(dfOdd)

This formula get 0.89664... , close to 90% of the odd transactions have insufficient funds.
Since we want accounts of insufficient funds before transaction. why use " <= " not. " < " ?
( The latter one get 0.21762... )

Regards,
Steven

@JannesKlaas
Hi Jannes,

Thank you for the amazing book and the tutorials, I really appreciate the time and effort you have put into the book.

I am wondering if there is somewhere that you have posted the answers of the exercieses for us to double check?
Since this is completely self-study, having answers to look up would be helpful to let me know that I am at least in the right direction.

Thanks.

@JannesKlaas
Hi Jannes,
In chapater 1,
...
A deeper network
...

Helper function to plot a decision boundary.

If you don't fully understand this function don't worry, it just generates the contour plot below.

def plot_decision_boundary(pred_func):
# Set min and max values and give it some padding
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
h = 0.01
# Generate a grid of points with distance h between them
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# Predict the function value for the whole gid
Z = pred_func(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
# Plot the contour and training examples
plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)
plt.scatter(X[:, 0], X[:, 1], c=y.flatten(), cmap=plt.cm.Spectral)

NameError Traceback (most recent call last)
/tmp/ipykernel_27/694468409.py in
15
16 #Plot the decision boundary
---> 17 plot_decision_boundary(lambda x: predict(model, x))
18 plt.title("Decision Boundary for hidden layer size 3")
19

/tmp/ipykernel_27/2764471563.py in plot_decision_boundary(pre_func)
114 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
115 #Predict the function value for the whole gid
--> 116 Z = pred_func(np.c_[xx_ravel(), yy_ravel()])
117 Z = Z.reshape(xx.shape)
118 #Plot the contour and training examples

NameError: name 'pred_func' is not defined

I keep getting this error, trying to solve it but havent be able to, I am using python 3.0 from mini-conda, thanks.

epoch = 5000 # Number of games played in training, I found the model needs about 4,000 games till it plays well

# Train the model
# For simplicity of the noteb
hist = train(model, epoch, verbose=1)

print("Training done")

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-11-1e110d42c25b> in <module>
      3 # Train the model
      4 # For simplicity of the noteb
----> 5 hist = train(model, epoch, verbose=1)
      6 
      7 print("Training done")

<ipython-input-10-b5900a520e40> in train(model, epochs, verbose)
     12         game_over = False
     13         # get initial input
---> 14         input_t = env.observe()
     15 
     16         while not game_over:

<ipython-input-2-a9f37a9957fc> in observe(self)
     57 
     58     def observe(self):
---> 59         canvas = self._draw_state()
     60         return canvas.reshape((1, -1))
     61 

<ipython-input-2-a9f37a9957fc> in _draw_state(self)
     36         canvas[state[0], state[1]] = 1  # draw fruit
     37 
---> 38         canvas[-1, state[2] -1:state[2] + 2] = 1  # draw basket
     39 
     40         return canvas

TypeError: only integer scalar arrays can be converted to a scalar index