I am trying to input a 7 band .tif image to a keras input layer.
input_img = Input(shape=(246, 177, 7))
The tif image is 246 x 177 and has 7 bands.
I am using rasterio to read the image as follows:
with rio.open(landsat_post_fire_path) as src:
landsat_post_fire = src.read()
This gives me 7 arrays of shape 177, 246
Since i have only one image, I am inserting it into an array:
x_train = np.expand_dims(landsat_post_fire, axis=0)
My complete autoencoder looks as follows:
input_size = 43542 #246 x 177
hidden_size = 20000
code_size = 5000
input_img = Input(shape=(246, 177, 7))
hidden_1 = Dense(hidden_size, activation='relu')(input_img)
code = Dense(code_size, activation='relu', activity_regularizer=l1(10e-6))(hidden_1)
hidden_3 = Dense(hidden_size, activation='relu')(code)
output_img = Dense(input_size, activation='sigmoid')(hidden_3)
autoencoder = Model(input_img, output_img)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
autoencoder.fit(x_train, x_train, epochs=1)
When I finally run the code, I get the following error:
Error when checking input: expected input_1 to have shape (246, 177, 7) but got array with shape (7, 177, 246)
Am I missing how the input shape is working?
I don't understand what seems to be the issue here. I would be grateful if someone can help explaining how the Input understands the dimension.
EDIT
I added the following line after reading the image:
landsat_post_fire=np.moveaxis(landsat_post_fire,0,-1)
But now the error is:
Error when checking target: expected dense_4 to have shape (177, 246, 43542) but got array with shape (177, 246, 7)
I think this is not the right way to tell the input about the number of bands in the image?
Related
I have created my own custom dataset (with 2 classes) with the following code:
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
ds_train = tf.keras.preprocessing.image_dataset_from_directory(
'C:/Users/mydir/Source_Images/',
labels = 'inferred', # from subfolders in alphabetical order
label_mode = "int",
class_names = ["CVS", "No_CVS"],
color_mode = 'rgb',
batch_size = 2,
image_size = (224, 224),
shuffle = True, # randomized order of images
seed = 123, #set the seed if train, valid images are the same when you run again
validation_split = 0.1,
subset = "training"
)
df_train results in:
<BatchDataset shapes: ((None, 224, 224, 3), (None,)), types: (tf.float32, tf.int32)>
Now, I want to visualize my data by looking at 9 images:
for i, (image, label) in enumerate(ds_train.take(9)):
ax = plt.subplot(3, 3, i + 1)
plt.imshow(image.numpy().astype("uint8"))
plt.axis("off")
However, I get the following error:
line 61, in
plt.imshow(image.numpy().astype("uint8"))
TypeError: Invalid shape (2, 224, 224, 3) for image data
I'm looking for a way to resolve this, and be able to plot my images with matplotlib.
EDIT:
More importantly, it seems that the data of the data cannot be used when training the model either as I get this error:
ValueError: Input 0 is incompatible with layer EfficientNet: expected shape=(None, 224, 224, 3), found shape=(2, None, 224, 224, 3)
After running the Keras example code I found here (where I created ds_train with the image_dataset_from_directory instead of the tdsf.load() function).
So I think there is something going wrong in the way I created the ds_train. Any resolutions are very welcome.
It seems like you are leaving the batch_size in, when you do:
plt.imshow(image.numpy().astype("uint8"))
With your original code you still won't be able to see 9 images because of your batch_size. I think it will be fine if you do it like:
No errors should be thrown like TypeError: Invalid shape...:
plt.imshow(image[i].numpy().astype("uint8"))
Furthermore you can do following to see batch_size:
for img_batch_size, labels_batch_size in train_df:
print(img_batch_size.shape)
print(labels_batch_size.shape)
For your case img_batch_size.shape should print (2,224,224,3) where this tuple corresponds to image tensor.
For input_shape problem, you need to add your model so we can see what's wrong with input_shape.
I am trying to augment (random crop) images while loading them using a tensorflow Dataset.
I am getting this error when I call the method tf.image.random_crop in the mapped function:
ValueError: Dimensions must be equal, but are 4 and 3 for '{{node random_crop/GreaterEqual}} = GreaterEqual[T=DT_INT32](random_crop/Shape, random_crop/size)' with input shapes: [4], [3].
In order to reproduce the error, just place some png images in the directory:
./img/class0/
Then run this code:
import os
import tensorflow as tf
train_set_raw = tf.keras.preprocessing.image_dataset_from_directory('./img',label_mode=None,validation_split=None,batch_size=32)
def augment(tensor):
tensor = tf.cast(x=tensor, dtype=tf.float32)
tensor = tf.divide(x=tensor, y=tf.constant(255.))
tensor = tf.image.random_crop(value=tensor, size=(256, 256, 3))
return tensor
train_set_raw = train_set_raw.map(augment).batch(32)
If I specify the batch size explicitly,
tensor = tf.image.random_crop(value=tensor, size=(32,256, 256, 3))
the error can be sorted. However, if you try to fit a model with a dataset created with a fixed batch size, you will get an error:
tensorflow.python.framework.errors_impl.InvalidArgumentError: assertion failed: [Need value.shape >= size, got ] [1 256 256 3] [32 256 256 3]
[[{{node random_crop/Assert/Assert}}]]
Try using a batch size of 1:
tensor = tf.image.random_crop(value=tensor, size=(1,256, 256, 3))
But I don't think you should mix high-level data loaders with a lower level tf.data.Dataset. Try using only the latter.
import tensorflow as tf
image_dir = r'C:\Users\user\Pictures'
files = tf.data.Dataset.list_files(image_dir + '\\*jpg')
def load(filepath):
image = tf.io.read_file(filepath)
image = tf.image.decode_image(image)
return image
ds = files.map(load)
def augment(tensor):
tensor = tf.cast(x=tensor, dtype=tf.float32)
tensor = tf.divide(x=tensor, y=tf.constant(255.))
tensor = tf.image.random_crop(value=tensor, size=(100, 100, 3))
random_target = tf.random.uniform((1,), dtype=tf.int32, maxval=2)
return tensor, random_target
train_set_raw = ds.map(augment).batch(32)
model = tf.keras.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(8, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
model.compile(loss='binary_crossentropy', optimizer='adam')
history = model.fit(train_set_raw)
I'm trying to reshape a Tensorflow model's input along the batch dimension. I want to combine some of the batch samples into a time-series so I can feed it into an LSTM layer.
Specifically, I have 1024 samples and I'd like to put them into groups of 64 timesteps with the result being 16 batches of 64 timesteps, each timestep having the original 24 features.
#input tensor is (1024, 24)
inputLayer = Input(shape=(24,))
#I want it to be (16, 64, 24)
reshapedLayer = layers.Reshape([64, 24])(inputLayer)
lstmLayer = layers.LSTM(128, activation='relu')(reshapedLayer)
This compiles but throws a runtime error
tensorflow.python.framework.errors_impl.InvalidArgumentError:
Input to reshape is a tensor with 24576 values, but the requested shape has 1572864
I understand what the error is telling me, but I'm not sure the right way to go about fixing it.
Perhaps this could work for you:
import tensorflow as tf
inputs = tf.keras.layers.Input(shape=(24,))
x = tf.reshape(inputs, (16, 64, 24))
x = tf.keras.layers.LSTM(128, activation='relu')(x)
model = tf.keras.Model(inputs=inputs, outputs=x)
# dummy data
inputs = tf.random.uniform(shape=(1024, 24))
outputs = model(inputs)
Replacing the Reshape layer with tf.reshape.
I am trying to extract features from a convolution layer of the VGGFace model, using TensorFlow & Keras.
This is my code:
# Layer Features
layer_name = 'conv1_2' # Edit this line
vgg_model = VGGFace() # Pooling: None, avg or max
out = vgg_model.get_layer(layer_name).output
vgg_model_new = Model(vgg_model.input, out)
def main():
img = image.load_img('myimage.jpg', target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = utils.preprocess_input(x, version=1)
preds = vgg_model_new.predict(x)
print('Predicted:', utils.decode_predictions(preds))
exit(0)
However, at the print('Predicted:', utils.decode_predictions(preds)) line I am getting the following error:
Message=decode_predictions expects a batch of predictions (i.e. a
2D array of shape (samples, 2622)) for V1 or (samples, 8631) for
V2.Found array with shape: (1, 224, 224, 64)
I just want to extract features, I don't need to classify my images at this point. This code is based on https://github.com/rcmalli/keras-vggface
You shouldn't use utils.decode_predictions(preds) there because it's only for classification. You can see the definition of the function here https://github.com/rcmalli/keras-vggface/blob/master/keras_vggface/utils.py#L66
If you want to print the features, use print('Predicted:',preds)
I am running a CNN for classification of medical scans using Keras and transfer learning with imagenet and InceptionV3. I am building the model with some practice data of size X_train = (624, 128, 128, 1) and Y_train = (624, 2).
I am trying to resize the input_tensor to suit the shape of my images (128 x 128 x 1) using the below code.
input_tensor = Input(shape=(128, 128, 1))
base_model = InceptionV3(input_tensor=input_tensor,weights='imagenet',include_top=False)
Doing this I get a value error:
ValueError: Dimension 0 in both shapes must be equal, but are 3 and 32. Shapes
are [3,3,1,32] and [32,3,3,3]. for 'Assign_753' (op: 'Assign') with input
shapes: [3,3,1,32], [32,3,3,3]
Is there a way to allow this model to accept my images in their format?
Edit:
For what its worth, here is the code to generate the training data.
X = []
Y = []
for subj, subj_slice in slices.items():
# X.extend([s[:, :, np.newaxis, np.newaxis] for s in slice])
subj_slice_norm = [((imageArray - np.min(imageArray)) / np.ptp(imageArray)) for imageArray in subj_slice]
X.extend([s[ :, :, np.newaxis] for s in subj_slice_norm])
subj_status = labels_df['deadstatus.event'][labels_df['PatientID'] == subj]
subj_status = np.asanyarray(subj_status)
#print(subj_status)
Y.extend([subj_status] * len(subj_slice))
X = np.stack(X, axis=0)
Y = to_categorical(np.stack(Y, axis=0))]
n_samp_train = int(X.shape[0]*0.8)
X_train, Y_train = X[:n_samp_train], Y[:n_samp_train]
Edit2:
I think the other alternative would be to take my X which is shape (780, 128, 128, 1), clone each of the 780 images and append two as dummies. Is this possible? Resulting in (780, 128, 128, 3).
We can use the existing keras layers to convert the existing image shape to the expected shape for the pre-trained model rather than using the numpy for replicating channels. As replicating channels before training may consume 3x the memory, but integrating this processing at runtime will save up a lot of memory.
You can proceed this way.
Step 1: Create a Keras Model that converts your input images to the shape that can be fed as the input for the base_model as follows:
from keras.models import Model
from keras.layers import RepeatVector, Input, Reshape
inputs = Input(shape=(128, 128, 1))
reshaped1 = Reshape(target_shape=((128 * 128 * 1,)))(inputs)
repeated = RepeatVector(n=3)(reshaped1)
reshaped2 = Reshape(target_shape=(3, 128, 128))(repeated)
input_model = Model(inputs=inputs, outputs=reshaped2)
Step 2: Define pre-trained model InceptionV3 as follows:
base_model = InceptionV3(input_tensor=input_model.output, weights='imagenet', include_top=False)
Step 3: Combine both the models as follows:
combined_model = Model(inputs=input_model.input, outputs=base_model.output)
The advantage of this method is that the keras model itself will take care of the image processing stuff like channel replication at runtime. Thus, we need not replicate the image channels by ourselves with numpy and the results will be memory efficient.