def doNN(data):
global submit_work
data_fft = np.fft.fft(data)
data_scaled = scaleData(data_fft)
data_scaled_reshaped = data_scaled
data_scaled_reshaped.shape = (3, 3000)
prediction = str(np.sum(score(data_scaled_reshaped)) / 3)
print("Prediction: %s, publishing..." % (prediction))
myData = {'healthy': prediction}
client.publishEvent("0.16.2", "lorenz", "status", "json", myData)
if submit_work:
submit_work = False
parts_data["ZzEP8"] = json.dumps(myData)
submitAll(email, secret, key, parts_data)
print("Submitting to grader: %s" % (json.dumps(myData)))
print("Done")
# In[34]:
part = "O5cR9"
secret = "HNKTKFFyRCl3BrmY"
parts_data = {}
parts_data["0dXlH"] = json.dumps({
"number_of_neurons_layer1": 0,
"number_of_neurons_layer2": 0,
"number_of_neurons_layer3": 0,
"number_of_epochs": 0
})
parts_data["O5cR9"] = json.dumps({"dim": dim, "samples": samples})
parts_data["ZzEP8"] = None
submitAll(email, secret, key, parts_data)
# To observe how training works we just print the loss during training
# In[89]:
class LossHistory(Callback):
def on_train_begin(self, logs={}):
self.losses = []
def on_batch_end(self, batch, logs={}):
sys.stdout.write(str(logs.get('loss')) + str(', '))
sys.stdout.flush()
self.losses.append(logs.get('loss'))
dim = 3000 #### your code here ###
samples = 3 #### your code here ###
# ### Submission
#
# Now it’s time to submit your first solution. Please make sure that the secret variable contains a valid submission token. You can obtain it from the courser web page of the course using the grader section of this assignment.
#
# In[20]:
part = "O5cR9"
secret = "8s4TiWZVfTYyoRIp"
submitAll(
email, secret, key,
dict((p, json.dumps({}) if p != part else json.dumps({
"dim": dim,
"samples": samples
})) for p in all_parts))
# To observe how training works we just print the loss during training
# In[21]:
class LossHistory(Callback):
def on_train_begin(self, logs={}):
self.losses = []
def on_batch_end(self, batch, logs={}):
sys.stdout.write(str(logs.get('loss')) + str(', '))
sys.stdout.flush()
plt.show()
# Congratulations, you are done! The following code submits your solution to the grader. Again, please update your token from the grader's submission page on Coursera
# In[15]:
get_ipython().system('rm -f rklib.py')
get_ipython().system(
'wget https://raw.githubusercontent.com/IBM/coursera/master/rklib.py')
# In[17]:
from rklib import submitAll
import json
key = "S5PNoSHNEeisnA6YLL5C0g"
email = "*****@*****.**"
token = "cuV16XmPypbDcP7f"
# In[18]:
parts_data = {}
parts_data["iLdHs"] = json.dumps(str(type(getListForHistogramAndBoxPlot())))
parts_data["xucEM"] = json.dumps(len(getListForHistogramAndBoxPlot()))
parts_data["IyH7U"] = json.dumps(str(type(getListsForRunChart())))
parts_data["MsMHO"] = json.dumps(len(getListsForRunChart()[0]))
submitAll(email, token, key, parts_data)
# In[ ]:
