I am getting this error while trying to train my tensor-flow model:
ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 800, 800, 1), found shape=(None, 640000, 1)
This is labels.shape:
(100,)
And this is imgs.shape(after expanding the dimensions):
(100, 640000, 1)
Each image is 800x800 pixels and has 1 color channel but when i do print(imgs[0].shape) it gives me:
(640000, 1)
How can i make the shape of imgs_train: (100, 800, 800, 1) instead of (100, 640000, 1)
This is the full code:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from bidict import bidict
from tensorflow import keras
from tensorflow.keras import layers
from sklearn.utils import shuffle
from sklearn.metrics import confusion_matrix
ENCODER = bidict({
'1': 1, '2': 2, '3': 3, '4': 4, '5': 5, '6': 6,
'7': 7, '8': 8, '9': 9, '10': 10
})
labels = np.load('data/labels.npy')
labels = np.array([ENCODER[x] for x in labels])
print(labels.shape)
imgs = np.load('data/images.npy')
imgs = imgs.astype("float32") / 255
print(imgs.shape)
imgs = np.expand_dims(imgs, -1)
print(imgs.shape)
labels, imgs = shuffle(labels, imgs)
split = .75
labels_train = labels[:int(len(labels) * split)]
labels_test = labels[int(len(labels) * split):]
imgs_train = imgs[:int(len(imgs) * split)]
imgs_test = imgs[int(len(imgs) * split):]
batch_size = 32
epochs = 10
model = keras.Sequential([
keras.Input(shape=(800, 800, 1)),
layers.Conv2D(256, kernel_size=5, activation='relu'),
layers.MaxPooling2D(pool_size=2),
layers.Dropout(0.3),
layers.Conv2D(512, kernel_size=5, activation='relu'),
layers.MaxPooling2D(pool_size=2),
layers.Dropout(0.3),
layers.Conv2D(1024, kernel_size=5, activation='relu'),
layers.MaxPooling2D(pool_size=2),
layers.Dropout(0.3),
layers.Flatten(),
layers.Dense(len(ENCODER)+1, activation='softmax')
])
early_stopping = keras.callbacks.EarlyStopping(monitor="val_accuracy", patience=2)
optimizer = keras.optimizers.Adam()
model.compile(loss='sparse_categorical_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
model.fit(imgs_train,
labels_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(imgs_test, labels_test),
callbacks=[early_stopping])
model.save("alphabet_detection.h5")
