I wanted to know how to apply a previously unseen image to a previously saved CNN model and see how it classifies it ?
Code (My attempt)
from keras.models import load_model
from keras.preprocessing import image
import numpy as np
img_path = '/Users/eoind/code/1.jpg'
model = load_model('food.h5')
model.summary()
img = image.load_img(img_path, target_size=(100,100))
image = image.img_to_array(img)
image = np.expand_dims(image, axis=0)
print(image.shape)
images = np.vstack([image])
print("classifying images..")
image_class = model.predict_classes(images)
print(image_class)
iPython Console Error
ValueError: Error when checking : expected dense_1_input to have 2 dimensions, but got array with shape (1, 100, 100, 3)
The error says that the input shape of your network does not match the shape of your image. It seems like the first layer of your network is a dense layer, which is a fully-connected layer and it expects the input to be of shape (batch_size, num_of_neurons_in_the_bottom), but your are giving it an image with shape (batch_size, height, width, channels). Here is a checklist for troubleshooting your problem:
Has the model at least loaded? If the above error happens during loading, then your model is broken (perhaps not properly saved). If not, continue through the checklist...
In programming, always output the values of your variables for debugging! What is the value of model.summary()? Are you sure that the input shape of your network is (100, 100, 3)? Is the first layer a convolutional one?
If the first layer is dense (which is fully-connected), check the training code on how you feed images to the model - maybe your image should be re-shaped or somehow pre-processed?
Related
import tensorflow as tf
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape = (28,28)),
tf.keras.layers.Dense(128, activation = 'relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10)
])
This is the code for the model, which I have trained using the mnist dataset. What I want to do is to then pass a 28x28 png image to the predict() method, which is not working. The code for the prediction is:
img = imageio.imread('image_0.png')
prediction = model.predict(img, batch_size = 1)
which produces the error
ValueError: Error when checking input: expected flatten_input to have shape (28, 28) but got array with shape (28, 3)
I have been stuck on this problem for a few days, but I can't find the correct way to pass an image into the predict method. Any help?
Predict function makes predictions over a batch of image. You should include batch dimension (first dimension) to your img, even to predict a single example.
You need something like this:
img = imageio.imread('image_0.png')
img = np.expand_dims(img, axis=0)
prediction = model.predict(img)
As #desertnaut says, seems you are using a RGB image, so your first layer should use input_shape = (28,28,3). Therefore, img parameter of predict function should have (1,28,28,3) shape.
In your case, img parameter of predict function has (28,28,3) shape, thus predict function took the first dimension as number of images, and could not match the other two dimensions to the input_shape of the first layer.
I've searched through all the solutions related to this, and I still can't figure out how to shape my training data so Tensorflow accepts it.
My training data is a numpy array of shape (21005, 48, 48), where the 21005 is number of elements and the 48,48 is a 48x48 grayscale image.
model.add(tf.keras.layers.Conv2D(64, kernel_size=3,activation='relu',input_shape=(48,48,1)))
model.add(tf.keras.layers.Conv2D(32, kernel_size=3,activation='relu'))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(7, activation='softmax'))
model.compile(optimizer='adam',
loss = 'sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(image_train, emotion_train,batch_size=BATCH_SIZE,epochs=EPOCHS, verbose=1)
When I run the fit function, however, it returns an error stating:
ValueError: Error when checking input: expected conv2d_input to have 4 dimensions, but got array with shape (21005, 48, 48)
This leads me to think I'm formatting the input data incorrectly, or missing something regarding how Keras and TF actually pass the input image into the input layer. I've tried adding the extra dimension to the input shape to allow for a channel in a 2d Conv layer, as well as reshaping the images themselves to no avail. Any advice?
Reshape your training data to have 4-dimensions before calling model.fit() such as:
image_train = np.reshape(image_train, (21005, 48, 48, 1))
This is needed because the first Conv2D layer expects an image to have an input_shape of (48,48,1)
When you made your preprocessing, you might have read the image in grayscale mode with a library OpenCV/PIL.
When you read them, your library considers a grayscale image of size (48,48), not a (48,48,1), hence the issue that you have.
Solve the issue as soon as possible, not before feeding to your model; in your code, wherever you read those images, before appending to your list/arrays, ensure the right shape of the array is picked. You can see down below an OpenCV example:
image = cv2.imread(filepath, 0)
#Before this np_expand_dims, image has shape (48,48)
image = np.expand_dims(image , axis=2)
#After this step, image has shape (48,48,1)
I'm having some problems making masking work with a keras RNN written in Functional API. The idea is to mask a tensor, zero-padded, with shape (batch_size, timesteps, 100) and feed it into a SimpleRNN. Right now I have the following:
input = keras.layers.Input(shape=(None, 100))
mask_layer = keras.layers.Masking(mask_value=0.)
mask = mask_layer(input)
rnn = keras.layers.SimpleRNN(20)
x = rnn(input, mask=mask)
However, this does not work, because it raises the following InvalidArgumentError:
InvalidArgumentError: Dimension 1 in both shapes must be equal, but are 20 and 2000. Shapes are [?,20] and [?,2000]. for 'Select' (op: 'Select') with input shapes: [?,2000], [?,20], [?,20].
By changing my Input's shape into (None, 1) - a sequential input where each element is a single integer, instead of n-dimensional embeddings - I've gotten this code to work. I've also gotten the same idea to work with the Sequential API, but I cannot do this, as my final model will have multiple inputs and outputs. I also do not want to force my Input's shape to be (None, 1), as I want to swap out different embedding models (Word2Vec, etc) during preprocessing, which means my Inputs will be embedding vectors from the start.
Can anyone help me with using masks with RNNs when using keras's functional API?
According to Masking and Padding with Keras, you won't need to manually set mask on the RNN layer, in the following code the RNN layer will automatically receive the mask.
import keras
input_layer = keras.layers.Input(shape=(None, 100))
masked_layer = keras.layers.Masking(mask_value=0.)(input_layer)
rnn_layer = keras.layers.SimpleRNN(20)(masked_layer)
I have loaded images to train my model on recognizing one feature in those images.
Xtrain is a numpy ndarray of shape (1380,200,200,3 ) containing 1380 images sized 200 by 200pixels in RGB format
Ytrain has targets. shape (1380,2)
When I train my model (model.fit(Xtrain,Ytrain)) I seem to get a value error on everyone of the layers. As if the input was both Xtrain then Ytrain...
ValueError: Error when checking target: expected batch_normalization_24 to have 4 dimensions, but got array with shape (1380, 2)
Image:
The shape of Keras's batch normalizer layer's output is the same as its input. Since you have only two labels, your final layer in a Sequential model should generate two outputs. You can consider adding a Dense layer like:
model.add(Dense(2), activation='relu')
I also recommend to check your model's architecture using print(model.summary()) and make sure that inputs and outputs match with your dataset and vice versa.
I have trained a convolutional3d model using code
Im trying to get the prediction as follows,
import cv2
from keras.models import Sequential, load_model
import numpy as np
#create an empty frame
frames = []
#defince row, col
img_rows,img_cols,img_depth=16,16,15
cap = cv2.VideoCapture('run.avi')
fps = cap.get(5)
#Use only first 15 frames for prediction
for k in range(15):
ret, frame = cap.read()
frame=cv2.resize(frame,(img_rows,img_cols),interpolation=cv2.INTER_AREA)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
frames.append(gray)
#preprocess
input = np.array(frames)
ipt=np.rollaxis(np.rollaxis(input,2,0),2,0)
reshape_frames = np.expand_dims(ipt, axis=0)
#run prediction
model = load_model('current.h5')
preds = model.predict(reshape_frames)
print(preds)
but it fires the following error,
ValueError: Error when checking : expected conv3d_1_input to have 5
dimensions, but got array with shape (1, 16, 16, 15)
How can I be able to sort this out?
See in the docs for convolutional 3D layers:
Input shape
5D tensor with shape: (samples, channels, conv_dim1, conv_dim2, conv_dim3) if data_format='channels_first' or 5D tensor with shape: (samples, conv_dim1, conv_dim2, conv_dim3, channels) if data_format='channels_last'.
So what is bascially happening is that the input shape you provide to your first conv 3D layer does not fit to the expected input.
To solve this you could go as follows:
change the provided input, so that it matches the expected input (also take into account the data_format as noted above). As it looks in your code, you don't use the img_depth information at all. You basically provide a 2D image to a 3D conv net.
Use a 2D convnet and create a new model