Why do my model gives always same outputs for different inputs

Here is some main parts of my code

datagen=ImageDataGenerator(preprocessing_function=tf.keras.applications.inception_resnet_v2.preprocess_input, rescale=1./255.) # I did rescale

attention_layer,map2 = SoftAttention(aggregate=True,m=16,concat_with_x=False,ch=int(conv.shape[-1]),name='soft_attention')(conv)
attention_layer=(MaxPooling2D(pool_size=(2, 2),padding="same")(attention_layer))
conv=(MaxPooling2D(pool_size=(2, 2),padding="same")(conv))
conv = concatenate([conv,attention_layer])

conv  = Activation('relu')(conv)
conv = Dropout(0.4)(conv)

output = Flatten()(conv)
output = Dense(7, activation='softmax')(output)
model = Model(inputs=irv2.input, outputs=output)

opt1=tf.keras.optimizers.Adam(lr=0.01)
model.compile(optimizer=opt1,
             loss='categorical_crossentropy',
             metrics=['accuracy'])
class_weights = {   
                    0: 1.0,  # akiec
                    1: 1.0,  # bcc
                    2: 1.0,  # bkl
                    3: 1.5,  # df
                    4: 1.0,  # mel
                    5: 1.0,  # nv
                    6: 1.0,  # vasc
                }
    
Earlystop = EarlyStopping(monitor='val_loss', mode='min',patience=70, min_delta=0.001)
    history = model.fit(train_batches,
                        steps_per_epoch=(len(train_df)/10),
                        epochs=100,
                        verbose=1,
                        validation_data=test_batches,validation_steps=len(test_df)/batch_size,callbacks=[checkpoint,Earlystop],class_weight=class_weights)

predictions = []
for i in range(len(arr)):
  p = model.predict(arr[i].reshape(1,299,299,3))
  print(p)
  predictions.append(np.argmax(p))

but I got:

[[0. 0. 0. 0. 0. 0. 1.]]
[[0. 0. 0. 0. 0. 0. 1.]]
[[0. 0. 0. 0. 0. 0. 1.]]
[[0. 0. 0. 0. 0. 0. 1.]]
[[0. 0. 0. 0. 0. 0. 1.]]
[[0. 0. 0. 0. 0. 0. 1.]]
[[0. 0. 0. 0. 0. 0. 1.]]
[[0. 0. 0. 0. 0. 0. 1.]]
[[0. 0. 0. 0. 0. 0. 1.]]
[[0. 0. 0. 0. 0. 0. 1.]]
[[0. 0. 0. 0. 0. 0. 1.]]
[[1.0111562e-35 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00
  0.0000000e+00 1.0000000e+00]]

and every time like this.