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Flashcard 7626434940172

Tags

#tensorflow #tensorflow-certificate

Question

# Calculate MSE "by hand" in steps - identify functions

abs_err = tf.abs(tf.subtract(tf.cast(y_test, dtype=tf.float32), tf.squeeze(y_pred)))
sq_abs_err = [...](abs_err, abs_err)
sq_abs_err
tf.math.reduce_mean(sq_abs_err)



<tf.Tensor: shape=(), dtype=float32, numpy=155.11417>

Answer

tf.multiply

status	not learned	measured difficulty	37% [default]	last interval [days]
repetition number in this series	0	memorised on		scheduled repetition
scheduled repetition interval		last repetition or drill

Calculate MSE "by hand" in steps - identify functions
# Calculate MSE "by hand" in steps - identify functions abs_err = tf.abs(tf.subtract(tf.cast(y_test, dtype=tf.float32), tf.squeeze(y_pred))) sq_abs_err = tf.multiply(abs_err, abs_err) sq_abs_err tf.math.reduce_mean(sq_abs_err) <tf.Tensor: shape=(), dtype=float32, numpy=155.11417>

Flashcard 7626436775180

Tags

#conv2D #convolution #tensorflow #tensorflow-certificate

Question

Step 1 is to gather the data. You'll notice that there's a bit of a change here in that the training data needed to be reshaped. That's because the first convolution expects a single tensor containing everything, so instead of 60,000 28x28x1 items in a list, we have a single [...dimensions?] list that is 60,000x28x28x1,

Answer

status	not learned	measured difficulty	37% [default]	last interval [days]
repetition number in this series	0	memorised on		scheduled repetition
scheduled repetition interval		last repetition or drill

Parent (intermediate) annotation

Open it
that the training data needed to be reshaped. That's because the first convolution expects a single tensor containing everything, so instead of 60,000 28x28x1 items in a list, we have a single <span>4D list that is 60,000x28x28x1, <span>

Original toplevel document

Convolution Neural Network - introduction
Step 1 is to gather the data. You'll notice that there's a bit of a change here in that the training data needed to be reshaped. That's because the first convolution expects a single tensor containing everything, so instead of 60,000 28x28x1 items in a list, we have a single 4D list that is 60,000x28x28x1, and the same for the test images. If you don't do this, you'll get an error when training as the Convolutions do not recognize the shape. import tensorflow as tf mnist = tf.keras.datase

Flashcard 7626845719820

Tags

#tensorflow #tensorflow-certificate

Question

# Calculate MSE "by hand" in steps - identify functions

abs_err = tf.abs(tf.subtract(tf.cast(y_test, dtype=tf.float32), [...](y_pred)))
sq_abs_err = tf.multiply(abs_err, abs_err)
sq_abs_err
tf.math.reduce_mean(sq_abs_err)



<tf.Tensor: shape=(), dtype=float32, numpy=155.11417>

Answer

tf.squeeze

status	not learned	measured difficulty	37% [default]	last interval [days]
repetition number in this series	0	memorised on		scheduled repetition
scheduled repetition interval		last repetition or drill

Flashcard 7627275635980

Tags

#has-images #tensorflow #tensorflow-certificate

[unknown IMAGE 7626420784396]

Question

Load model

loaded_model_SM = tf.keras.[...].load_model('/content/best_model_3_SavedModel')
loaded_model_SM.summary()

Answer

models

status	not learned	measured difficulty	37% [default]	last interval [days]
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scheduled repetition interval		last repetition or drill

Parent (intermediate) annotation

Open it
Load model loaded_model_SM = tf.keras.models.load_model('/content/best_model_3_SavedModel') loaded_model_SM.summary()

Original toplevel document

TfC 01 regression
# Save the entire model using SavedModel model_3.save("best_model_3_SavedModel") # SavedModel is in principle protobuff)pb file # Save model in HDF5 format: model_3.save("best_model_3_HDF5.h5") <span>Load model loaded_model_SM = tf.keras.models.load_model('/content/best_model_3_SavedModel') loaded_model_SM.summary() <span>

Edited, memorised or added to reading queue

on 18-May-2024 (Sat)

Flashcard 7626434940172

Flashcard 7626436775180

Parent (intermediate) annotation

Original toplevel document

Flashcard 7626845719820

Flashcard 7627275635980

Load model

Parent (intermediate) annotation

Original toplevel document