When I try to go back using chainer, it fits a little

Overview

Source code

Where I got stuck

python


#target is correct answer data
#mnist.If it is py, it is written like ↓
target = diabetes['target'].astype(np.float32) #With this, it will not work if the number of mini batches is 2 or more

If it is ↑, a Value Error like ↓ will appear (13 is batch size).

ValueError: non-broadcastable output operand with shape (1,30) doesn't match the broadcast shape (13,30)

As a result of trial and error, it worked.

python


#I have to reshape
target = diabetes['target'].astype(np.float32).reshape(len(diabetes['target']), 1)

Changes from example / mnist.py

environment

Regarding the installation of chainer

change point

data set

model

python


n_units   = 30
model = FunctionSet(l1=F.Linear(10, n_units),                                 
                    l2=F.Linear(n_units, n_units),
                    l3=F.Linear(n_units, 1)) 

Adjustment of learning rate

python


optimizer = optimizers.AdaDelta(rho=0.9)

Error function and evaluation method

python


# Neural net architecture
def forward(x_data, y_data, train=True):
    x, t = Variable(x_data), Variable(y_data)
    h1 = F.dropout(F.relu(model.l1(x)),  train=train)
    h2 = F.dropout(F.relu(model.l2(h1)), train=train)
    y  = model.l3(h2)
    #Returns mean squared error and prediction results
    #The prediction result is returned because the correlation coefficient is calculated later with the prediction result and the correct answer data.
    return F.mean_squared_error(y, t), y

python


pearson = np.corrcoef(np.asarray(preds).reshape(len(preds),), np.asarray(y_test).reshape(len(preds),))

Impressions

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