# on 12-Apr-2024 (Fri)

#### 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])

#### 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])

fit

status measured difficulty not learned 37% [default] 0

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])

optimizer

status measured difficulty not learned 37% [default] 0

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])

tensorflow.keras

status measured difficulty not learned 37% [default] 0

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])

tensorflow.keras.layers

status measured difficulty not learned 37% [default] 0

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])

Sequential

status measured difficulty not learned 37% [default] 0

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])

loss

status measured difficulty not learned 37% [default] 0

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])

compile

status measured difficulty not learned 37% [default] 0

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])

predict

status measured difficulty not learned 37% [default] 0

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])

xs, ys

status measured difficulty not learned 37% [default] 0

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])

epochs

status measured difficulty not learned 37% [default] 0

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])

Dense

status measured difficulty not learned 37% [default] 0

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])

input_shape

status measured difficulty not learned 37% [default] 0

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])

Dense

status measured difficulty not learned 37% [default] 0

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])

[1]

status measured difficulty not learned 37% [default] 0

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])

Sequential

status measured difficulty not learned 37% [default] 0

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 

#### 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


callbacks.Callback

status measured difficulty not learned 37% [default] 0

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


on_epoch_end

status measured difficulty not learned 37% [default] 0

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


logs

status measured difficulty not learned 37% [default] 0

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


get

status measured difficulty not learned 37% [default] 0

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


stop_training

status measured difficulty not learned 37% [default] 0

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


model

status measured difficulty not learned 37% [default] 0

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