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Annotation 7624056769804

Tensorflow basics - typical flow of model building
#tensorflow #tensorflow-certificate

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
import numpy as np

model = Sequential(Dense(1, input_shape=[1]))
model.compile(optimizer='sgd', loss='mean_squared_error')
xs = np.array([1,5,12,-1,10], dtype=float)
ys = np.array([5,13,27,1,23], dtype=float)
model.fit(xs, ys, epochs=500)
model.predict(x=[15])
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on



Flashcard 7624058604812

Tags
#tensorflow #tensorflow-certificate
Question

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
import numpy as np

model = Sequential(Dense(1, input_shape=[1]))
model.compile(optimizer='sgd', loss='mean_squared_error')
xs = np.array([1,5,12,-1,10], dtype=float)
ys = np.array([5,13,27,1,23], dtype=float)
model.[...](xs, ys, epochs=500)
model.predict(x=[15])

Answer
fit

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Tensorflow basics - typical flow of model building
del = Sequential(Dense(1, input_shape=[1])) model.compile(optimizer='sgd', loss='mean_squared_error') xs = np.array([1,5,12,-1,10], dtype=float) ys = np.array([5,13,27,1,23], dtype=float) model.<span>fit(xs, ys, epochs=500) model.predict(x=[15]) <span>






Flashcard 7624060177676

Tags
#tensorflow #tensorflow-certificate
Question

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
import numpy as np

model = Sequential(Dense(1, input_shape=[1]))
model.compile([...]='sgd', loss='mean_squared_error')
xs = np.array([1,5,12,-1,10], dtype=float)
ys = np.array([5,13,27,1,23], dtype=float)
model.fit(xs, ys, epochs=500)
model.predict(x=[15])

Answer
optimizer

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Tensorflow basics - typical flow of model building
from tensorflow.keras import Sequential from tensorflow.keras.layers import Dense import numpy as np model = Sequential(Dense(1, input_shape=[1])) model.compile(optimizer='sgd', loss='mean_squared_error') xs = np.array([1,5,12,-1,10], dtype=float) ys = np.array([5,13,27,1,23], dtype=float) model.fit(xs, ys, epochs=500) model.predict(x=[15])






Flashcard 7624061750540

Tags
#tensorflow #tensorflow-certificate
Question

from [...] import Sequential
from tensorflow.keras.layers import Dense
import numpy as np

model = Sequential(Dense(1, input_shape=[1]))
model.compile(optimizer='sgd', loss='mean_squared_error')
xs = np.array([1,5,12,-1,10], dtype=float)
ys = np.array([5,13,27,1,23], dtype=float)
model.fit(xs, ys, epochs=500)
model.predict(x=[15])

Answer
tensorflow.keras

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Tensorflow basics - typical flow of model building
from tensorflow.keras import Sequential from tensorflow.keras.layers import Dense import numpy as np model = Sequential(Dense(1, input_shape=[1])) model.compile(optimizer='sgd', loss='mean_squared_error') xs






Flashcard 7624063323404

Tags
#tensorflow #tensorflow-certificate
Question

from tensorflow.keras import Sequential
from [...] import Dense
import numpy as np

model = Sequential(Dense(1, input_shape=[1]))
model.compile(optimizer='sgd', loss='mean_squared_error')
xs = np.array([1,5,12,-1,10], dtype=float)
ys = np.array([5,13,27,1,23], dtype=float)
model.fit(xs, ys, epochs=500)
model.predict(x=[15])

Answer
tensorflow.keras.layers

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Tensorflow basics - typical flow of model building
from tensorflow.keras import Sequential from tensorflow.keras.layers import Dense import numpy as np model = Sequential(Dense(1, input_shape=[1])) model.compile(optimizer='sgd', loss='mean_squared_error') xs = np.array([1,5,12,-1,10], dtype=float) ys = n






Flashcard 7624064896268

Tags
#tensorflow #tensorflow-certificate
Question

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
import numpy as np

model = [...](Dense(1, input_shape=[1]))
model.compile(optimizer='sgd', loss='mean_squared_error')
xs = np.array([1,5,12,-1,10], dtype=float)
ys = np.array([5,13,27,1,23], dtype=float)
model.fit(xs, ys, epochs=500)
model.predict(x=[15])

Answer
Sequential

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Tensorflow basics - typical flow of model building
from tensorflow.keras import Sequential from tensorflow.keras.layers import Dense import numpy as np model = Sequential(Dense(1, input_shape=[1])) model.compile(optimizer='sgd', loss='mean_squared_error') xs = np.array([1,5,12,-1,10], dtype=float) ys = np.array([5,13,27,1,23], dtype=float) model.fit(xs,






Flashcard 7624065944844

Tags
#tensorflow #tensorflow-certificate
Question

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
import numpy as np

model = Sequential(Dense(1, input_shape=[1]))
model.compile(optimizer='sgd', [...]='mean_squared_error')
xs = np.array([1,5,12,-1,10], dtype=float)
ys = np.array([5,13,27,1,23], dtype=float)
model.fit(xs, ys, epochs=500)
model.predict(x=[15])

Answer
loss

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Tensorflow basics - typical flow of model building
from tensorflow.keras import Sequential from tensorflow.keras.layers import Dense import numpy as np model = Sequential(Dense(1, input_shape=[1])) model.compile(optimizer='sgd', loss='mean_squared_error') xs = np.array([1,5,12,-1,10], dtype=float) ys = np.array([5,13,27,1,23], dtype=float) model.fit(xs, ys, epochs=500) model.predict(x=[15])






Flashcard 7624066993420

Tags
#tensorflow #tensorflow-certificate
Question

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
import numpy as np

model = Sequential(Dense(1, input_shape=[1]))
model.[...](optimizer='sgd', loss='mean_squared_error')
xs = np.array([1,5,12,-1,10], dtype=float)
ys = np.array([5,13,27,1,23], dtype=float)
model.fit(xs, ys, epochs=500)
model.predict(x=[15])

Answer
compile

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Tensorflow basics - typical flow of model building
from tensorflow.keras import Sequential from tensorflow.keras.layers import Dense import numpy as np model = Sequential(Dense(1, input_shape=[1])) model.compile(optimizer='sgd', loss='mean_squared_error') xs = np.array([1,5,12,-1,10], dtype=float) ys = np.array([5,13,27,1,23], dtype=float) model.fit(xs, ys, epochs=500) model.predict(x=[15]) </s






Flashcard 7624068041996

Tags
#tensorflow #tensorflow-certificate
Question

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
import numpy as np

model = Sequential(Dense(1, input_shape=[1]))
model.compile(optimizer='sgd', loss='mean_squared_error')
xs = np.array([1,5,12,-1,10], dtype=float)
ys = np.array([5,13,27,1,23], dtype=float)
model.fit(xs, ys, epochs=500)
model.[...](x=[15])

Answer
predict

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Tensorflow basics - typical flow of model building
t_shape=[1])) model.compile(optimizer='sgd', loss='mean_squared_error') xs = np.array([1,5,12,-1,10], dtype=float) ys = np.array([5,13,27,1,23], dtype=float) model.fit(xs, ys, epochs=500) model.<span>predict(x=[15]) <span>






Flashcard 7624069090572

Tags
#tensorflow #tensorflow-certificate
Question

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
import numpy as np

model = Sequential(Dense(1, input_shape=[1]))
model.compile(optimizer='sgd', loss='mean_squared_error')
xs = np.array([1,5,12,-1,10], dtype=float)
ys = np.array([5,13,27,1,23], dtype=float)
model.fit([...], epochs=500)
model.predict(x=[15])

Answer
xs, ys

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Tensorflow basics - typical flow of model building
= Sequential(Dense(1, input_shape=[1])) model.compile(optimizer='sgd', loss='mean_squared_error') xs = np.array([1,5,12,-1,10], dtype=float) ys = np.array([5,13,27,1,23], dtype=float) model.fit(<span>xs, ys, epochs=500) model.predict(x=[15]) <span>






Flashcard 7624070139148

Tags
#tensorflow #tensorflow-certificate
Question

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
import numpy as np

model = Sequential(Dense(1, input_shape=[1]))
model.compile(optimizer='sgd', loss='mean_squared_error')
xs = np.array([1,5,12,-1,10], dtype=float)
ys = np.array([5,13,27,1,23], dtype=float)
model.fit(xs, ys, [...]=500)
model.predict(x=[15])

Answer
epochs

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Tensorflow basics - typical flow of model building
tial(Dense(1, input_shape=[1])) model.compile(optimizer='sgd', loss='mean_squared_error') xs = np.array([1,5,12,-1,10], dtype=float) ys = np.array([5,13,27,1,23], dtype=float) model.fit(xs, ys, <span>epochs=500) model.predict(x=[15]) <span>






Flashcard 7624071187724

Tags
#tensorflow #tensorflow-certificate
Question

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
import numpy as np

model = Sequential([...](1, input_shape=[1]))
model.compile(optimizer='sgd', loss='mean_squared_error')
xs = np.array([1,5,12,-1,10], dtype=float)
ys = np.array([5,13,27,1,23], dtype=float)
model.fit(xs, ys, epochs=500)
model.predict(x=[15])

Answer
Dense

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Tensorflow basics - typical flow of model building
from tensorflow.keras import Sequential from tensorflow.keras.layers import Dense import numpy as np model = Sequential(Dense(1, input_shape=[1])) model.compile(optimizer='sgd', loss='mean_squared_error') xs = np.array([1,5,12,-1,10], dtype=float) ys = np.array([5,13,27,1,23], dtype=float) model.fit(xs, ys, ep






Flashcard 7624072236300

Tags
#tensorflow #tensorflow-certificate
Question

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
import numpy as np

model = Sequential(Dense(1, [...]=[1]))
model.compile(optimizer='sgd', loss='mean_squared_error')
xs = np.array([1,5,12,-1,10], dtype=float)
ys = np.array([5,13,27,1,23], dtype=float)
model.fit(xs, ys, epochs=500)
model.predict(x=[15])

Answer
input_shape

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Tensorflow basics - typical flow of model building
from tensorflow.keras import Sequential from tensorflow.keras.layers import Dense import numpy as np model = Sequential(Dense(1, input_shape=[1])) model.compile(optimizer='sgd', loss='mean_squared_error') xs = np.array([1,5,12,-1,10], dtype=float) ys = np.array([5,13,27,1,23], dtype=float) model.fit(xs, ys, epochs=500) model






Flashcard 7624073284876

Tags
#tensorflow #tensorflow-certificate
Question

from tensorflow.keras import Sequential
from tensorflow.keras.layers import [...]
import numpy as np

model = Sequential(Dense(1, input_shape=[1]))
model.compile(optimizer='sgd', loss='mean_squared_error')
xs = np.array([1,5,12,-1,10], dtype=float)
ys = np.array([5,13,27,1,23], dtype=float)
model.fit(xs, ys, epochs=500)
model.predict(x=[15])

Answer
Dense

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Tensorflow basics - typical flow of model building
from tensorflow.keras import Sequential from tensorflow.keras.layers import Dense import numpy as np model = Sequential(Dense(1, input_shape=[1])) model.compile(optimizer='sgd', loss='mean_squared_error') xs = np.array([1,5,12,-1,10], dtype=float) ys = np.array([5,13






Flashcard 7624074333452

Tags
#tensorflow #tensorflow-certificate
Question

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
import numpy as np

model = Sequential(Dense(1, input_shape=[...]))
model.compile(optimizer='sgd', loss='mean_squared_error')
xs = np.array([1,5,12,-1,10], dtype=float)
ys = np.array([5,13,27,1,23], dtype=float)
model.fit(xs, ys, epochs=500)
model.predict(x=[15])

Answer
[1]

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Tensorflow basics - typical flow of model building
from tensorflow.keras import Sequential from tensorflow.keras.layers import Dense import numpy as np model = Sequential(Dense(1, input_shape=[1])) model.compile(optimizer='sgd', loss='mean_squared_error') xs = np.array([1,5,12,-1,10], dtype=float) ys = np.array([5,13,27,1,23], dtype=float) model.fit(xs, ys, epochs=500) model.pre






Flashcard 7624075382028

Tags
#tensorflow #tensorflow-certificate
Question

from tensorflow.keras import [...]
from tensorflow.keras.layers import Dense
import numpy as np

model = Sequential(Dense(1, input_shape=[1]))
model.compile(optimizer='sgd', loss='mean_squared_error')
xs = np.array([1,5,12,-1,10], dtype=float)
ys = np.array([5,13,27,1,23], dtype=float)
model.fit(xs, ys, epochs=500)
model.predict(x=[15])

Answer
Sequential

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Tensorflow basics - typical flow of model building
from tensorflow.keras import Sequential from tensorflow.keras.layers import Dense import numpy as np model = Sequential(Dense(1, input_shape=[1])) model.compile(optimizer='sgd', loss='mean_squared_error') xs = np.array([1,5,1






Annotation 7624080100620

Tensorflow - callbacks
#tensorflow #tensorflow-certificate 

import tensorflow as tf

#stop training after reaching accuract of 0.99
class MyCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs={}):
    if logs.get('accuracy')>=0.99:
      print('\nAccuracy 0.99 achieved')
      self.model.stop_training = True

statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on



Flashcard 7624083508492

Tags
#tensorflow #tensorflow-certificate
Question

import tensorflow as tf

#stop training after reaching accuract of 0.9
class MyCallback(tf.keras.[...]):
  def on_epoch_end(self, epoch, logs={}):
    if logs.get('accuracy')>=0.99:
      print('\nAccuracy 0.99 achieved')
      self.model.stop_training = True

Answer
callbacks.Callback

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Tensorflow - callbacks
import tensorflow as tf #stop training after reaching accuract of 0.9 class MyCallback(tf.keras.callbacks.Callback): def on_epoch_end(self, epoch, logs={}): if logs.get('accuracy')>=0.99: print('\nAccuracy 0.99 achieved') self.model.stop_training = True






Flashcard 7624086129932

Tags
#tensorflow #tensorflow-certificate
Question

import tensorflow as tf

#stop training after reaching accuract of 0.99
class MyCallback(tf.keras.callbacks.Callback):
  def [...](self, epoch, logs={}):
    if logs.get('accuracy')>=0.99:
      print('\nAccuracy 0.99 achieved')
      self.model.stop_training = True

Answer
on_epoch_end

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Tensorflow - callbacks
import tensorflow as tf #stop training after reaching accuract of 0.99 class MyCallback(tf.keras.callbacks.Callback): def on_epoch_end(self, epoch, logs={}): if logs.get('accuracy')>=0.99: print('\nAccuracy 0.99 achieved') self.model.stop_training = True






Flashcard 7624087178508

Tags
#tensorflow #tensorflow-certificate
Question

import tensorflow as tf

#stop training after reaching accuract of 0.99
class MyCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs={}):
    if [...].get('accuracy')>=0.99:
      print('\nAccuracy 0.99 achieved')
      self.model.stop_training = True

Answer
logs

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Tensorflow - callbacks
import tensorflow as tf #stop training after reaching accuract of 0.99 class MyCallback(tf.keras.callbacks.Callback): def on_epoch_end(self, epoch, logs={}): if logs.get('accuracy')>=0.99: print('\nAccuracy 0.99 achieved') self.model.stop_training = True






Flashcard 7624088227084

Tags
#tensorflow #tensorflow-certificate
Question

import tensorflow as tf

#stop training after reaching accuract of 0.99
class MyCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs={}):
    if logs.[...]('accuracy')>=0.99:
      print('\nAccuracy 0.99 achieved')
      self.model.stop_training = True

Answer
get

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Tensorflow - callbacks
import tensorflow as tf #stop training after reaching accuract of 0.99 class MyCallback(tf.keras.callbacks.Callback): def on_epoch_end(self, epoch, logs={}): if logs.get('accuracy')>=0.99: print('\nAccuracy 0.99 achieved') self.model.stop_training = True






Flashcard 7624089275660

Tags
#tensorflow #tensorflow-certificate
Question

import tensorflow as tf

#stop training after reaching accuract of 0.99
class MyCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs={}):
    if logs.get('accuracy')>=0.99:
      print('\nAccuracy 0.99 achieved')
      self.model.[...] = True

Answer
stop_training

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Tensorflow - callbacks
r reaching accuract of 0.99 class MyCallback(tf.keras.callbacks.Callback): def on_epoch_end(self, epoch, logs={}): if logs.get('accuracy')>=0.99: print('\nAccuracy 0.99 achieved') self.model.<span>stop_training = True <span>






Flashcard 7624090324236

Tags
#tensorflow #tensorflow-certificate
Question

import tensorflow as tf

#stop training after reaching accuract of 0.99
class MyCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs={}):
    if logs.get('accuracy')>=0.99:
      print('\nAccuracy 0.99 achieved')
      self.[...].stop_training = True

Answer
model

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Tensorflow - callbacks
g after reaching accuract of 0.99 class MyCallback(tf.keras.callbacks.Callback): def on_epoch_end(self, epoch, logs={}): if logs.get('accuracy')>=0.99: print('\nAccuracy 0.99 achieved') self.<span>model.stop_training = True <span>






Flashcard 7624091372812

Tags
#tensorflow #tensorflow-certificate
Question

import tensorflow as tf

#stop training after reaching accuract of 0.99
class MyCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs={}):
    if logs.get('accuracy')>=0.99:
      print('\nAccuracy 0.99 achieved')
      [...].model.stop_training = True

Answer
self

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Tensorflow - callbacks
aining after reaching accuract of 0.99 class MyCallback(tf.keras.callbacks.Callback): def on_epoch_end(self, epoch, logs={}): if logs.get('accuracy')>=0.99: print('\nAccuracy 0.99 achieved') <span>self.model.stop_training = True <span>