# on 18-May-2024 (Sat)

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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 measured difficulty not learned 37% [default] 0

Calculate MSE &quot;by hand&quot; 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
4D

status measured difficulty not learned 37% [default] 0

#### Parent (intermediate) annotation

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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 measured difficulty not learned 37% [default] 0

Calculate MSE &quot;by hand&quot; 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 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 measured difficulty not learned 37% [default] 0

#### Parent (intermediate) annotation

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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>