0 0 1),ġ00| loss= 'sparse_categorical_crossentropy',ġ01| metrics=)ġ02| early_stop = tf.(monitor= 'val_loss', patience= 1 0)ġ03| history = model.fit(train_generator,ġ09| 110| 111| #MAKE PREDICTIONS FOR VALIDATION SET AND CALCULATE ACCURACY 112| predictions = model.predict(test_generator,ġ15| predictions = np.argmax(predictions, axis=- 1)ġ16| val = val. You can check about steps_per_epoch in here.5| from import SequentialĦ| from import Dense, Flatten, Dropout, MaxPooling2Dĩ| from keras_preprocessing.image import ImageDataGeneratorġ0| from efficientnet.tfkeras import EfficientNetB0ġ1| from trics import accuracy_scoreġ2| 13| CLASSES = ġ4| EPOCHS = 2 5 15| 16| IMG_DIM = 2 5 5 17| BATCH_SIZE = 6 4 18| 19| #UNCOMMENT AND EDIT FOR ADDITIONAL AUGMENTATIONS 20| train_datagen = ImageDataGenerator(Ģ2| #horizontal_flip=True, 23| #vertical_flip=True, 24| #featurewise_std_normalization=True, 25| #rotation_range=180, 26| #shear_range=2.0, 27| #brightness_range=(0.0, 0.2) 28| #zoom_range=(0,0.25, 29| #channel_shift_range=30, 30| #width_shift_range=0.2, 31| #height_shift_range=0.2 32| )ģ3| 34| test_datagen = ImageDataGenerator(ģ8| base_model = EfficientNetB0(input_shape=(IMG_DIM, IMG_DIM, 3),Ĥ1| 42| base_ainable = False 43| model = Sequential(Ĥ5| tf.2D(),ĥ3| Dense( len(CLASSES), activation= 'softmax')]ĥ6| 57| train = pd.read_csv( 'data/train.csv')ĥ8| val = pd.read_csv( 'data/validation.csv')Ħ0| train = train.sample(frac= 1).reset_index(drop=True)Ħ1| val = val.sample(frac= 1).reset_index(drop=True)Ħ2| 63| train = train.astype(str)Ħ4| val = val.astype(str)Ħ5| test = test.astype(str)Ħ6| 67| train_generator = train_datagen.flow_from_dataframe(train,Ħ9| x_col= 'image_id', #IMAGE FILENAME IN TRAIN DF 70| y_col= 'label', #IMAGE TARGET IN TRAIN DF 71| target_size=(IMG_DIM, IMG_DIM ),ħ4| val_generator = test_datagen.flow_from_dataframe(val,ħ6| x_col= 'image_id', #IMAGE FILENAME IN VAL DF 77| y_col= 'label', #IMAGE TARGET IN VAL DF 78| shuffle=False,Ĩ2| 83| test_generator = test_datagen.flow_from_dataframe(test,Ĩ5| x_col= 'image_id', #IMAGE FILENAME IN TEST DF 86| y_col= 'label', #IMAGE TARGET IN TEST DF 87| shuffle=False,ĩ5| 96| STEP_SIZE_TRAIN=train_generator.n//train_generator.batch_sizeĩ7| STEP_SIZE_VALID=val_generator.n//val_generator.batch_sizeĩ8| 99| model. Moreover, you've got to specify steps_per_epoch argument to some number if you are using repeat() method otherwise it means to run forever. # 3/3 - 0s 24ms/step - loss: 1.1743 - mse: 1.1743īesides, if you have a huge dataset, you don't really need to use repeat() method of tf.data.API. Sample_predicted_batch = simple_model(sample_input_batch) # check whether the model really produces the thing I expect Sample_input_batch, sample_output_batch = next(iter(train_ds)) # check whether this dataset really produces the things I want Train_ds = train_ds.batch(batch_size=32, drop_remainder=True) In my simple reproductive example, import tensorflow as tf SIZE 10 def generator (): feats np.random.normal (0, 1, SIZE) labels np.random.normal (0, 1, SIZE) yield feats, labels def inputfuncgen (): shapes (SIZE, SIZE) dataset tf. (generatorgenerator, outputtypes (tf.float32, tf.float32), outputshapesshapes) dataset dataset.batch (10) dataset. If you checked the resulting input_batch and output_batch from your train_dataset, it has got to work. I wonder if the preparation of the TF dataset is something I am doing correctly. In theory, 1 batch of data should be 4 matrices 480x480 with 1 channel, (4, 480, 480, 1) as input and also (4, 480, 480, 1) as output.īut I don't obtain anythinig from the training and I am quite suspicious. History = model.fit(train_dataset, validation_data=val_dataset, epochs=100) The model, called rnc, goes here (omitted for brevity, ConvLSTM and Conv3D involved). I am not sure about the necessity of using repeat. Train_dataset = my_input_fn(arr_train_ids) ) #using TF2.3, output_signature not valid Here’s an example of how you might use tf. (): def generator(): for i in range(10): yield i dataset tf.(generator, outputtypestf.int32) In this example, we define a generator function that yields integers from 0 to 9. Then, following a post from here, I do: def my_input_fn(array_passed):ĭataset = tf._generator(lambda: my_gen(array_passed), Where obtain_data is just open the file, load 4 480x480 matrices and reshape to (4, 480, 480, 1). Since I have a huge dataset, I have written this generator: def my_gen(array_passed): I am training a NN to read 4 images and predict the next 4.
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