Feeding a 2D image to a TensorFlow CNN for image classification

I think you need to change a few things: the input shape to your model does not need the batch_size, it will be inferred during training. Change it to (400, 400, 3). Second, if you are working with binary labels, you need to change your loss function to tf.keras.losses.BinaryCrossentropy and your metric to tf.keras.metrics.BinaryAccuracy or simply accuracy. Furthermore, your output layer should have one output node instead of two: tf.keras.layers.Dense(1)

Here is a running example based on your code:

import numpy as np
import tensorflow as tf

no_of_samples = 250
BATCH_SIZE = 16
SHUFFLE_BUFFER_SIZE = 50

data, labels = np.random.random((no_of_samples, 400, 400, 3)), np.random.randint(2, size=no_of_samples)

dataset = tf.data.Dataset.from_tensor_slices((data, labels)).shuffle(SHUFFLE_BUFFER_SIZE)
test_dataset = dataset.take(50).batch(BATCH_SIZE)
train_dataset = dataset.skip(50).batch(BATCH_SIZE)

model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(200, 5, strides=3, activation='relu', input_shape=(400, 400, 3)),
    tf.keras.layers.Conv2D(100, 5, strides=2, activation="relu"),
    tf.keras.layers.Conv2D(50, 5, activation="relu"),
    tf.keras.layers.Conv2D(25, 3, activation="relu"),
    tf.keras.layers.MaxPooling2D(3),
    tf.keras.layers.Conv2D(50, 3, activation="relu"),
    tf.keras.layers.Conv2D(25, 3, activation="relu"),
    tf.keras.layers.MaxPooling2D(3),
    tf.keras.layers.Conv2D(50, 2, activation="relu"),
    tf.keras.layers.Conv2D(25, 2, activation="relu"),
    
    tf.keras.layers.GlobalMaxPooling2D(),

    # Finally, we add a classification layer.
    tf.keras.layers.Dense(1)
])

model.compile(optimizer=tf.keras.optimizers.RMSprop(),
              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
              metrics=['accuracy'])

model.fit(train_dataset, epochs=10, validation_data=test_dataset)

print('Labels shape -->',labels.shape)
print('Labels -->', labels)
Labels shape --> (250,)
Labels --> [1 0 0 0 0 1 0 1 0 1 1 1 0 1 1 0 0 1 0 0 1 0 0 1 0 1 1 0 0 1 1 1 0 1 1 0 0
 0 1 0 1 1 1 0 1 1 1 1 0 0 0 0 0 1 1 1 0 1 1 0 0 0 1 1 0 1 1 0 0 1 1 1 0 0
 1 0 1 1 1 1 1 1 1 1 0 0 1 1 0 1 1 1 1 0 1 1 0 0 0 1 0 1 1 1 0 1 0 1 1 0 1
 1 1 1 1 0 0 0 1 0 0 0 1 1 1 0 1 1 1 0 0 0 1 1 1 0 1 0 1 0 1 1 0 1 0 0 1 0
 1 1 1 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 1 1 1 0 0 0 0 1 1 0 0 1 1 1 0 0 0 0 1
 0 0 0 0 1 1 0 1 1 1 0 0 0 0 1 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 1
 0 0 1 1 1 1 1 0 0 0 1 0 0 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0]