I was performing a classification problem on some set of images, where my number of classes are three. Now since I am performing CNN, so it has a convolution layer and Pooling layer and then a few dense layers; the model parameters are shown below:
def baseline_model():
model = Sequential()
model.add(Conv2D(32, (5, 5), input_shape=(1, 100, 100), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(60, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
The model runs perfectly and shows me the accuracy and validation error, etc,. as shown below:
model = baseline_model()
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=20, verbose=1)
scores = model.evaluate(X_test, y_test, verbose=0)
print("CNN Error: %.2f%%" % (100-scores[1]*100))
Which gives me output:
Train on 514 samples, validate on 129 samples
Epoch 1/5
514/514 [==============================] - 23s 44ms/step - loss: 1.2731 - acc: 0.4202 - val_loss: 1.0349 - val_acc: 0.4419
Epoch 2/5
514/514 [==============================] - 18s 34ms/step - loss: 1.0172 - acc: 0.4416 - val_loss: 1.0292 - val_acc: 0.4884
Epoch 3/5
514/514 [==============================] - 17s 34ms/step - loss: 0.9368 - acc: 0.5817 - val_loss: 0.9915 - val_acc: 0.4806
Epoch 4/5
514/514 [==============================] - 18s 34ms/step - loss: 0.7367 - acc: 0.7101 - val_loss: 0.9973 - val_acc: 0.4961
Epoch 5/5
514/514 [==============================] - 17s 32ms/step - loss: 0.4587 - acc: 0.8521 - val_loss: 1.2328 - val_acc: 0.5039
CNN Error: 49.61%
The issue occurs in the prediction part.
So for my test images, for whom I need predictions; when I run model.predict(), it gives me this error:
TypeError: data type not understood
I can show the full error if required. And just to show, the shape of my training images and images I am finally using to predict on:
X_train.shape
(514, 1, 100, 100)
final.shape
(277, 1, 100, 100)
So I've no idea what this error means and what's the issue. Even the data type of my image values is same 'float32'. So the shape is same and data type is same, then why is this issue coming?
