6b --- gives the ame issues as 6a

[andysingal]

code:

import matplotlib.pylab as plt
import numpy as np
import tensorflow as tf

IMG_HEIGHT = 224
IMG_WIDTH = 224
IMG_CHANNELS = 3
CLASS_NAMES = 'daisy dandelion roses sunflowers tulips'.split()

class _Preprocessor:    
    def __init__(self):
        self.preproc_layers = tf.keras.Sequential([
            tf.keras.layers.Lambda(
                lambda img: tf.image.resize_with_pad(
                    img, 2*IMG_HEIGHT, 2*IMG_WIDTH), 
                input_shape=(None, None, 3)),
            tf.keras.layers.experimental.preprocessing.CenterCrop(
                height=IMG_HEIGHT, width=IMG_WIDTH)
        ])
    
    def read_from_tfr(self, proto):
        feature_description = {
            'image': tf.io.VarLenFeature(tf.float32),
            'shape': tf.io.VarLenFeature(tf.int64),
            'label': tf.io.FixedLenFeature([], tf.string, default_value=''),
            'label_int': tf.io.FixedLenFeature([], tf.int64, default_value=0),
        }
        rec = tf.io.parse_single_example(
            proto, feature_description
        )
        shape = tf.sparse.to_dense(rec['shape'])
        img = tf.reshape(tf.sparse.to_dense(rec['image']), shape)
        label_int = rec['label_int']
        return img, label_int
    
    def read_from_jpegfile(self, filename):
        # same code as in 05_create_dataset/jpeg_to_tfrecord.py
        img = tf.io.read_file(filename)
        img = tf.image.decode_jpeg(img, channels=IMG_CHANNELS)
        img = tf.image.convert_image_dtype(img, tf.float32)
        return img
      
    def preprocess(self, img):
        # add to a batch, call preproc, remove from batch
        x = tf.expand_dims(img, 0)
        x = self.preproc_layers(x)
        x = tf.squeeze(x, 0)
        return x
    
def create_preproc_dataset(pattern):
    preproc = _Preprocessor()
    trainds = tf.data.TFRecordDataset(
        [filename for filename in tf.io.gfile.glob(pattern)],
        compression_type='GZIP'
    ).map(preproc.read_from_tfr).map(
        lambda img, label: (preproc.preprocess(img), label))                             
    return trainds

def create_preproc_image(filename):
    preproc = _Preprocessor()
    img = preproc.read_from_jpegfile(filename)
    return preproc.preprocess(img)    

# preprocessing records read from a TensorFlow dataset
trainds = create_preproc_dataset('gs://practical-ml-vision-book/flowers_tfr/train-*')
f, ax = plt.subplots(1, 5, figsize=(15,15))
for idx, (img, label) in enumerate(trainds.take(5)):
    ax[idx].imshow((img.numpy()));

ERROR:
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
[<ipython-input-72-a567f27bc3c2>](https://localhost:8080/#) in <cell line: 2>()
      1 # preprocessing records read from a TensorFlow dataset
----> 2 trainds = create_preproc_dataset('gs://practical-ml-vision-book/flowers_tfr/train-*')
      3 f, ax = plt.subplots(1, 5, figsize=(15,15))
      4 for idx, (img, label) in enumerate(trainds.take(5)):
      5     ax[idx].imshow((img.numpy()));

24 frames
[/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py](https://localhost:8080/#) in _create_c_op(graph, node_def, inputs, control_inputs, op_def, extract_traceback)
   1971   except errors.InvalidArgumentError as e:
   1972     # Convert to ValueError for backwards compatibility.
-> 1973     raise ValueError(e.message)
   1974 
   1975   # Record the current Python stack trace as the creating stacktrace of this

ValueError: in user code:

    File "<ipython-input-71-ac9c68a069e7>", line 56, in None  *
        lambda img, label: (preproc.preprocess(img), label)
    File "<ipython-input-71-ac9c68a069e7>", line 47, in preprocess  *
        x = tf.squeeze(x, 0)

    ValueError: Can not squeeze dim[0], expected a dimension of 1, got 224 for '{{node Squeeze}} = Squeeze[T=DT_FLOAT, squeeze_dims=[0]](sequential_24/center_crop_16/cond/Identity)' with input shapes: [224,224,?].